From 4306f2c49c171077fc32228962a5431078e739e2 Mon Sep 17 00:00:00 2001
From: yhliang <429259365@qq.com>
Date: Tue, 18 Apr 2023 20:05:38 +0800
Subject: [PATCH 1/4] remove Indices and tables

---
 docs_m2met2/_build/doctrees/Baseline.doctree  | Bin 6239 -> 6269 bytes
 docs_m2met2/_build/doctrees/Contact.doctree   | Bin 4393 -> 4389 bytes
 docs_m2met2/_build/doctrees/Dataset.doctree   | Bin 14373 -> 14369 bytes
 .../_build/doctrees/Introduction.doctree      | Bin 15501 -> 14733 bytes
 .../_build/doctrees/Organizers.doctree        | Bin 24634 -> 10632 bytes
 docs_m2met2/_build/doctrees/Rules.doctree     | Bin 6931 -> 6927 bytes
 .../Track_setting_and_evaluation.doctree      | Bin 12733 -> 12729 bytes
 .../_build/doctrees/environment.pickle        | Bin 26348 -> 25953 bytes
 docs_m2met2/_build/doctrees/index.doctree     | Bin 6359 -> 4743 bytes
 docs_m2met2/_build/html/Baseline.html         |   4 ++--
 docs_m2met2/_build/html/Introduction.html     |   9 ++++----
 docs_m2met2/_build/html/Organizers.html       |   9 --------
 .../_build/html/_images/sa_asr_arch.png       | Bin 760286 -> 1014545 bytes
 .../_build/html/_sources/Baseline.md.txt      |   1 +
 .../_build/html/_sources/Introduction.md.txt  |  10 ++++-----
 .../_build/html/_sources/Organizers.md.txt    |  20 ------------------
 .../_build/html/_sources/index.rst.txt        |   7 ------
 docs_m2met2/_build/html/index.html            |   8 -------
 docs_m2met2/_build/html/searchindex.js        |   2 +-
 docs_m2met2/index.rst                         |   7 ------
 .../_build/doctrees/environment.pickle        | Bin 26046 -> 25645 bytes
 docs_m2met2_cn/_build/doctrees/index.doctree  | Bin 6085 -> 4473 bytes
 .../_build/html/_sources/index.rst.txt        |   7 ------
 docs_m2met2_cn/_build/html/index.html         |   8 -------
 docs_m2met2_cn/_build/html/searchindex.js     |   2 +-
 docs_m2met2_cn/_build/html/基线.html          |   2 +-
 docs_m2met2_cn/_build/html/数据集.html        |   2 +-
 docs_m2met2_cn/_build/html/简介.html          |   2 +-
 docs_m2met2_cn/_build/html/组委会.html        |  10 ++++-----
 docs_m2met2_cn/_build/html/规则.html          |   2 +-
 .../_build/html/赛道设置与评估.html           |   2 +-
 docs_m2met2_cn/index.rst                      |   7 ------
 32 files changed, 23 insertions(+), 98 deletions(-)

diff --git a/docs_m2met2/_build/doctrees/Baseline.doctree b/docs_m2met2/_build/doctrees/Baseline.doctree
index c30687bab28a3e11f51561ab2dbccdc213def38f..7765a4fb6a502a445543dd95211953addb63d68e 100644
GIT binary patch
delta 275
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delta 297
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diff --git a/docs_m2met2/_build/doctrees/Contact.doctree b/docs_m2met2/_build/doctrees/Contact.doctree
index bed2f74c570db64736ef0263e6d819ff01e299f3..459f62f520d1250732cc9d3d5d592b9400a28ace 100644
GIT binary patch
delta 24
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delta 29
lcmZ3gv{H$sfn}=PMiv`JMvKXgjG8R*CAkHgvlw^t0|0&@2$KK+

diff --git a/docs_m2met2/_build/doctrees/Dataset.doctree b/docs_m2met2/_build/doctrees/Dataset.doctree
index 841452cfbd34c538dcc79521fb680b343ac20950..b5d9e8bc4b457209d61d60ac1bd28030016f16d3 100644
GIT binary patch
delta 24
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delta 29
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diff --git a/docs_m2met2/_build/doctrees/Introduction.doctree b/docs_m2met2/_build/doctrees/Introduction.doctree
index a5119b7d70deef47389599fc924687193531e14c..e80c8f44a34609bd2cc3a325948348d918a4590f 100644
GIT binary patch
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GIT binary patch
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diff --git a/docs_m2met2/_build/doctrees/Rules.doctree b/docs_m2met2/_build/doctrees/Rules.doctree
index 4ac75811cc4dfc1e1e2f15f5639d599cfd36b85d..d2836b008ad974d0dec8e458b3535eee9fbd3e10 100644
GIT binary patch
delta 24
fcmbPi)^Enrz%rF(BZ~ziqtRqrM$OGxj4LGpRn7*h

delta 29
kcmeA-n{39?z%rF%BZ~ziquFFzMopIZlH7vLX^bl+0ebfcV*mgE

diff --git a/docs_m2met2/_build/doctrees/Track_setting_and_evaluation.doctree b/docs_m2met2/_build/doctrees/Track_setting_and_evaluation.doctree
index 12d3c2bc1dc7afb4ccfc33caa4ba9585cb7f9587..5829a02db70d6e8daa200d6404752b3ce2d5c6b8 100644
GIT binary patch
delta 24
gcmdm+yfc}lfo1BtjVwWojGmLj88tVzFj^V_0CHUj4*&oF

delta 29
lcmdm)yf>Mpfo1BZjVwWojJ}h@88unrOL7Y~H!xZn005u03C92c

diff --git a/docs_m2met2/_build/doctrees/environment.pickle b/docs_m2met2/_build/doctrees/environment.pickle
index 57b88c87057cf604fe37cb69bb37953bf7918349..3fd14ad19ec4be8816551b37d35de5ab14f64144 100644
GIT binary patch
delta 2056
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delta 2120
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diff --git a/docs_m2met2/_build/doctrees/index.doctree b/docs_m2met2/_build/doctrees/index.doctree
index 1cb0d3dd2b851d605f1246fb8aaaebdf1cba69ba..4b041ae8a6aa849316704467ada83b32b61fb52e 100644
GIT binary patch
delta 76
zcmca^*sjXjz%sQ)Xd~+uCPw4QyO=aLKWDP$*c>P*#>AAdeshlSeP)G>&WyI=9?rbP
f+|-iFg4E(EwNo-WJBu?UfplAjPVwee@gPP36BQgv

delta 1318
zcmb7^&1(};5Ww@DtTEaI8WWq=C{&Z$SOq^I9`q(Edhp;arkmuM*>$rG*<FkWsUEBb
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diff --git a/docs_m2met2/_build/html/Baseline.html b/docs_m2met2/_build/html/Baseline.html
index 282f23529..8893a1692 100644
--- a/docs_m2met2/_build/html/Baseline.html
+++ b/docs_m2met2/_build/html/Baseline.html
@@ -135,8 +135,8 @@
 </section>
 <section id="baseline-results">
 <h2>Baseline results<a class="headerlink" href="#baseline-results" title="Permalink to this heading">¶</a></h2>
-<p>The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy.
-<img alt="baseline result" src="_images/baseline_result.png" /></p>
+<p>The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy.</p>
+<p><img alt="baseline result" src="_images/baseline_result.png" /></p>
 </section>
 </section>
 
diff --git a/docs_m2met2/_build/html/Introduction.html b/docs_m2met2/_build/html/Introduction.html
index 54de72d3e..d75fa954e 100644
--- a/docs_m2met2/_build/html/Introduction.html
+++ b/docs_m2met2/_build/html/Introduction.html
@@ -128,10 +128,9 @@
 <section id="call-for-participation">
 <h2>Call for participation<a class="headerlink" href="#call-for-participation" title="Permalink to this heading">¶</a></h2>
 <p>Automatic speech recognition (ASR) and speaker diarization have made significant strides in recent years, resulting in a surge of speech technology applications across various domains. However, meetings present unique challenges to speech technologies due to their complex acoustic conditions and diverse speaking styles, including overlapping speech, variable numbers of speakers, far-field signals in large conference rooms, and environmental noise and reverberation.</p>
-<p>Over the years, several challenges have been organized to advance the development of meeting transcription, including the Rich Transcription evaluation and Computational Hearing in Multisource Environments (CHIME) challenges. The latest iteration of the CHIME challenge has a particular focus on distant automatic speech recognition (ASR) and developing systems that can generalize across various array topologies and application scenarios. However, while progress has been made in English meeting transcription, language differences remain a significant barrier to achieving comparable results in non-English languages, such as Mandarin.</p>
-<p>The Multimodal Information Based Speech Processing (MISP) and Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenges have been instrumental in advancing Mandarin meeting transcription. The MISP challenge seeks to address the problem of audio-visual distant multi-microphone signal processing in everyday home environments, while the M2MeT challenge focuses on tackling the speech overlap issue in offline meeting rooms.</p>
-<p>The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises two main tasks: speaker diarization and multi-speaker automatic speech recognition (ASR). The former involves identifying who spoke when in the meeting, while the latter aims to transcribe speech from multiple speakers simultaneously, which poses significant technical difficulties due to overlapping speech and acoustic interferences.</p>
-<p>Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. By attributing speech to specific speakers, this task aims to improve the accuracy and applicability of multi-talker ASR systems in real-world settings. The challenge provides detailed datasets, rules, evaluation methods, and baseline systems to facilitate reproducible research in this field. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying “who spoke what at when”. To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models’ performance and advance the state of the art in this area.</p>
+<p>Over the years, several challenges have been organized to advance the development of meeting transcription, including the Rich Transcription evaluation and Computational Hearing in Multisource Environments (CHIME) challenges. The latest iteration of the CHIME challenge has a particular focus on distant automatic speech recognition and developing systems that can generalize across various array topologies and application scenarios. However, while progress has been made in English meeting transcription, language differences remain a significant barrier to achieving comparable results in non-English languages, such as Mandarin. The Multimodal Information Based Speech Processing (MISP) and Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenges have been instrumental in advancing Mandarin meeting transcription. The MISP challenge seeks to address the problem of audio-visual distant multi-microphone signal processing in everyday home environments, while the M2MeT challenge focuses on tackling the speech overlap issue in offline meeting rooms.</p>
+<p>The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises two main tasks: speaker diarization and multi-speaker automatic speech recognition. The former involves identifying who spoke when in the meeting, while the latter aims to transcribe speech from multiple speakers simultaneously, which poses significant technical difficulties due to overlapping speech and acoustic interferences.</p>
+<p>Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying “who spoke what at when”. To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models’ performance and advance the state of the art in this area.</p>
 </section>
 <section id="timeline-aoe-time">
 <h2>Timeline(AOE Time)<a class="headerlink" href="#timeline-aoe-time" title="Permalink to this heading">¶</a></h2>
@@ -147,7 +146,7 @@
 </section>
 <section id="guidelines">
 <h2>Guidelines<a class="headerlink" href="#guidelines" title="Permalink to this heading">¶</a></h2>
-<p>Possible improved version: Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023.</p>
+<p>Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023.</p>
 <p>Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top three submissions to be included in the ASRU2023 Proceedings.</p>
 </section>
 </section>
diff --git a/docs_m2met2/_build/html/Organizers.html b/docs_m2met2/_build/html/Organizers.html
index e5bb748aa..f66c037f9 100644
--- a/docs_m2met2/_build/html/Organizers.html
+++ b/docs_m2met2/_build/html/Organizers.html
@@ -127,36 +127,27 @@
 <p><em><strong>Lei Xie, Professor, Northwestern Polytechnical University, China</strong></em></p>
 <p>Email: <a class="reference external" href="mailto:lxie&#37;&#52;&#48;nwpu&#46;edu&#46;cn">lxie<span>&#64;</span>nwpu<span>&#46;</span>edu<span>&#46;</span>cn</a></p>
 <a class="reference internal image-reference" href="_images/lxie.jpeg"><img alt="lxie" src="_images/lxie.jpeg" style="width: 20%;" /></a>
-<p>Lei Xie received the Ph.D. degree in computer science from Northwestern Polytechnical University, Xi’an, China, in 2004. From 2001 to 2002, he was with the Department of Electronics and Information Processing, Vrije Universiteit Brussel (VUB), Brussels, Belgium, as a Visiting Scientist. From 2004 to 2006, he was a Senior Research Associate with the Center for Media Technology, School of Creative Media, City University of Hong Kong, Hong Kong, China. From 2006 to 2007, he was a Postdoctoral Fellow with the Human-Computer Communications Laboratory (HCCL), The Chinese University of Hong Kong, Hong Kong, China. He is currently a Professor with School of Computer Science, Northwestern Polytechnical University, Xian, China and leads the Audio, Speech and Language Processing Group (ASLP&#64;NPU). He has published over 200 papers in referred journals and conferences, such as IEEE/ACM Transactions on Audio, Speech and Language Processing, IEEE Transactions on Multimedia, Interspeech, ICASSP, ASRU, ACL and ACM Multimedia. He has achieved several best paper awards in flagship conferences. His current research interests include general topics in speech and language processing, multimedia, and human-computer interaction. Dr. Xie is currently an associate editor (AE) of IEEE/ACM Trans. on Audio, Speech and language Processing. He has actively served as Chairs in many conferences and technical committees. He serves as an IEEE Speech and Language Processing
-Technical Committee Member.</p>
 <p><em><strong>Kong Aik Lee, Senior Scientist at Institute for Infocomm Research, A*Star, Singapore</strong></em></p>
 <p>Email: <a class="reference external" href="mailto:kongaik&#46;lee&#37;&#52;&#48;ieee&#46;org">kongaik<span>&#46;</span>lee<span>&#64;</span>ieee<span>&#46;</span>org</a></p>
 <a class="reference internal image-reference" href="_images/kong.png"><img alt="kong" src="_images/kong.png" style="width: 20%;" /></a>
-<p>Kong Aik Lee started off him career as a researcher, then a team leader and a  strategic planning manager, at the Institute Infocomm Research, A*STAR, Singapore, working on speaker and language recognition research. From 2018 to 2020, he spent two and a half years in NEC Corporation, Japan, focusing very much on voice biometrics and multi-modal biometrics products.  He is proud to work with a great team on voice biometrics featured on NEC Bio-Idiom platform. He returned to Singapore in July 2020, and now leading the speech and audio analytics research at the Institute for Infocomm Research, as a Senior Scientist and PI.  He also serve as an Editor for Elsevier Computer Speech and Language (since 2016), and was an Associate Editor for IEEE/ACM Transactions on Audio, Speech and Language Processing (2017 - 2021), and am an elected member of IEEE Speech and Language Technical Committee (2019 - 2021).</p>
 <p><em><strong>Zhijie Yan, Principal Engineer at Alibaba, China</strong></em>
 Email: <a class="reference external" href="mailto:zhijie&#46;yzj&#37;&#52;&#48;alibaba-inc&#46;com">zhijie<span>&#46;</span>yzj<span>&#64;</span>alibaba-inc<span>&#46;</span>com</a></p>
 <a class="reference internal image-reference" href="_images/zhijie.jpg"><img alt="zhijie" src="_images/zhijie.jpg" style="width: 20%;" /></a>
-<p>Zhijie Yan holds a PhD from the University of Science and Technology of China, and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). He is also an expert reviewer of top academic conferences and journals in the speech field. His research fields include speech recognition, speech synthesis, voiceprints, and speech interaction. His research results are applied in speech services provided by Alibaba Group and Ant Financial. He was awarded the title of “One of the Top 100 Grassroots Scientists” by the China Association for Science and Technology.</p>
 <p><em><strong>Shiliang Zhang, Senior Engineer at Alibaba, China</strong></em>
 Email: <a class="reference external" href="mailto:sly&#46;zsl&#37;&#52;&#48;alibaba-inc&#46;com">sly<span>&#46;</span>zsl<span>&#64;</span>alibaba-inc<span>&#46;</span>com</a></p>
 <a class="reference internal image-reference" href="_images/zsl.JPG"><img alt="zsl" src="_images/zsl.JPG" style="width: 20%;" /></a>
-<p>Shiliang Zhang graduated with a Ph.D. from the University of Science and Technology of China in 2017. His research areas mainly include speech recognition, natural language understanding, and machine learning. Currently, he has published over 40 papers in mainstream academic journals and conferences in the fields of speech and machine learning, and has applied for dozens of patents. After obtaining his doctorate degree, he joined the Alibaba Intelligent Speech team. He is currently leading the direction of speech recognition and fundamental technology at DAMO Academy’s speech laboratory.</p>
 <p><em><strong>Yanmin Qian, Professor, Shanghai Jiao Tong University, China</strong></em></p>
 <p>Email: <a class="reference external" href="mailto:yanminqian&#37;&#52;&#48;sjtu&#46;edu&#46;cn">yanminqian<span>&#64;</span>sjtu<span>&#46;</span>edu<span>&#46;</span>cn</a></p>
 <a class="reference internal image-reference" href="_images/qian.jpeg"><img alt="qian" src="_images/qian.jpeg" style="width: 20%;" /></a>
-<p>Yanmin Qian received the B.S. degree from the Department of Electronic and Information Engineering,Huazhong University of Science and Technology, Wuhan, China, in 2007, and the Ph.D. degree from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2012. Since 2013, he has been with the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), Shanghai, China, where he is currently an Associate Professor. From 2015 to 2016, he also worked as an Associate Research in the Speech Group, Cambridge University Engineering Department, Cambridge, U.K. He is a senior member of IEEE and a member of ISCA, and one of the founding members of Kaldi Speech Recognition Toolkit. He has published more than 110 papers on speech and language processing with 4000+ citations, including the top conference: ICASSP, INTERSPEECH and ASRU. His current research interests include the acoustic and language modeling in speech recognition, speaker and language recognition, key word spotting, and multimedia signal processing.</p>
 <p><em><strong>Zhuo Chen, Applied Scientist in Microsoft, USA</strong></em></p>
 <p>Email: <a class="reference external" href="mailto:zhuc&#37;&#52;&#48;microsoft&#46;com">zhuc<span>&#64;</span>microsoft<span>&#46;</span>com</a></p>
 <a class="reference internal image-reference" href="_images/chenzhuo.jpg"><img alt="chenzhuo" src="_images/chenzhuo.jpg" style="width: 20%;" /></a>
-<p>Zhuo Chen received the Ph.D. degree from Columbia University, New York, NY, USA, in 2017. He is currently a Principal Applied Data Scientist with Microsoft. He has authored or coauthored more than 80 papers in peer-reviewed journals and conferences with around 6000 citations. He is a reviewer or technical committee member for more than ten journals and conferences. His research interests include automatic conversation recognition, speech separation, diarisation, and speaker information extraction. He actively participated in the academic events and challenges, and won several awards. Meanwhile, he contributed to open-sourced datasets, such as WSJ0-2mix, LibriCSS, and AISHELL-4, that have been main benchmark datasets for multi-speaker processing research. In 2020, he was the Team Leader in 2020 Jelinek workshop, leading more than 30 researchers and students to push the state of the art in conversation transcription.</p>
 <p><em><strong>Jian Wu, Applied Scientist in Microsoft, USA</strong></em></p>
 <p>Email: <a class="reference external" href="mailto:wujian&#37;&#52;&#48;microsoft&#46;com">wujian<span>&#64;</span>microsoft<span>&#46;</span>com</a></p>
 <a class="reference internal image-reference" href="_images/wujian.jpg"><img alt="wujian" src="_images/wujian.jpg" style="width: 20%;" /></a>
-<p>Jian Wu received a master degree from Northwestern Polytechnical University, Xi’an, China, in 2020 and currently he is a Applied Scientist in Microsoft, USA. His research interests cover multi-channel signal processing, robust and multi-talker speech recognition, speech enhancement, dereverberation and separation. He has around 30 conference publications with a total citation over 1200. He participated in several challenges such as CHiME5, DNS 2020 and FFSVC 2020 and contributed to the open-sourced datasets including LibriCSS and AISHELL-4. He is also a reviewer for several journals and conferences such as ICASSP, SLT, TASLP and SPL.</p>
 <p><em><strong>Hui Bu, CEO, AISHELL foundation, China</strong></em></p>
 <p>Email: <a class="reference external" href="mailto:buhui&#37;&#52;&#48;aishelldata&#46;com">buhui<span>&#64;</span>aishelldata<span>&#46;</span>com</a></p>
 <a class="reference internal image-reference" href="_images/buhui.jpeg"><img alt="buhui" src="_images/buhui.jpeg" style="width: 20%;" /></a>
-<p>Hui Bu received his master degree in the Artificial Intelligence Laboratory of Korea University in 2014. He is the founder and the CEO of AISHELL and AISHELL foundation. He participated in the release of AISHELL 1 &amp; 2 &amp; 3 &amp; 4, DMASH and HI-MIA open source database project and is the co-founder of China Kaldi offline Technology Forum.</p>
 </section>
 
 
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diff --git a/docs_m2met2/_build/html/_sources/Baseline.md.txt b/docs_m2met2/_build/html/_sources/Baseline.md.txt
index c34679fd6..6f9609bde 100644
--- a/docs_m2met2/_build/html/_sources/Baseline.md.txt
+++ b/docs_m2met2/_build/html/_sources/Baseline.md.txt
@@ -9,4 +9,5 @@ We will release an E2E SA-ASR~\cite{kanda21b_interspeech} baseline conducted on
 
 ## Baseline results
 The results of the baseline system are shown in Table 3. The speaker profile adopts the oracle speaker embedding during training. However, due to the lack of oracle speaker label during evaluation, the speaker profile provided by an additional spectral clustering is used. Meanwhile, the results of using the oracle speaker profile on Eval and Test Set are also provided to show the impact of speaker profile accuracy. 
+
 ![baseline result](images/baseline_result.png)
\ No newline at end of file
diff --git a/docs_m2met2/_build/html/_sources/Introduction.md.txt b/docs_m2met2/_build/html/_sources/Introduction.md.txt
index c98e6ce7a..e1f9fc792 100644
--- a/docs_m2met2/_build/html/_sources/Introduction.md.txt
+++ b/docs_m2met2/_build/html/_sources/Introduction.md.txt
@@ -2,13 +2,11 @@
 ## Call for participation
 Automatic speech recognition (ASR) and speaker diarization have made significant strides in recent years, resulting in a surge of speech technology applications across various domains. However, meetings present unique challenges to speech technologies due to their complex acoustic conditions and diverse speaking styles, including overlapping speech, variable numbers of speakers, far-field signals in large conference rooms, and environmental noise and reverberation. 
 
-Over the years, several challenges have been organized to advance the development of meeting transcription, including the Rich Transcription evaluation and Computational Hearing in Multisource Environments (CHIME) challenges. The latest iteration of the CHIME challenge has a particular focus on distant automatic speech recognition (ASR) and developing systems that can generalize across various array topologies and application scenarios. However, while progress has been made in English meeting transcription, language differences remain a significant barrier to achieving comparable results in non-English languages, such as Mandarin.
+Over the years, several challenges have been organized to advance the development of meeting transcription, including the Rich Transcription evaluation and Computational Hearing in Multisource Environments (CHIME) challenges. The latest iteration of the CHIME challenge has a particular focus on distant automatic speech recognition and developing systems that can generalize across various array topologies and application scenarios. However, while progress has been made in English meeting transcription, language differences remain a significant barrier to achieving comparable results in non-English languages, such as Mandarin. The Multimodal Information Based Speech Processing (MISP) and Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenges have been instrumental in advancing Mandarin meeting transcription. The MISP challenge seeks to address the problem of audio-visual distant multi-microphone signal processing in everyday home environments, while the M2MeT challenge focuses on tackling the speech overlap issue in offline meeting rooms.
 
-The Multimodal Information Based Speech Processing (MISP) and Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenges have been instrumental in advancing Mandarin meeting transcription. The MISP challenge seeks to address the problem of audio-visual distant multi-microphone signal processing in everyday home environments, while the M2MeT challenge focuses on tackling the speech overlap issue in offline meeting rooms.
+The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises two main tasks: speaker diarization and multi-speaker automatic speech recognition. The former involves identifying who spoke when in the meeting, while the latter aims to transcribe speech from multiple speakers simultaneously, which poses significant technical difficulties due to overlapping speech and acoustic interferences.
 
-The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises two main tasks: speaker diarization and multi-speaker automatic speech recognition (ASR). The former involves identifying who spoke when in the meeting, while the latter aims to transcribe speech from multiple speakers simultaneously, which poses significant technical difficulties due to overlapping speech and acoustic interferences.
-
-Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. By attributing speech to specific speakers, this task aims to improve the accuracy and applicability of multi-talker ASR systems in real-world settings. The challenge provides detailed datasets, rules, evaluation methods, and baseline systems to facilitate reproducible research in this field. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying "who spoke what at when". To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models' performance and advance the state of the art in this area.
+Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying "who spoke what at when". To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models' performance and advance the state of the art in this area.
 
 ## Timeline(AOE Time)
 
@@ -22,6 +20,6 @@ Building on the success of the previous M2MeT challenge, we are excited to propo
 
 ## Guidelines
 
-Possible improved version: Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023. 
+Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023. 
 
 Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top three submissions to be included in the ASRU2023 Proceedings. 
diff --git a/docs_m2met2/_build/html/_sources/Organizers.md.txt b/docs_m2met2/_build/html/_sources/Organizers.md.txt
index 5aea55846..e16c803af 100644
--- a/docs_m2met2/_build/html/_sources/Organizers.md.txt
+++ b/docs_m2met2/_build/html/_sources/Organizers.md.txt
@@ -5,8 +5,6 @@ Email: [lxie@nwpu.edu.cn](mailto:lxie@nwpu.edu.cn)
 
 <img src="images/lxie.jpeg" alt="lxie" width="20%">
 
-Lei Xie received the Ph.D. degree in computer science from Northwestern Polytechnical University, Xi'an, China, in 2004. From 2001 to 2002, he was with the Department of Electronics and Information Processing, Vrije Universiteit Brussel (VUB), Brussels, Belgium, as a Visiting Scientist. From 2004 to 2006, he was a Senior Research Associate with the Center for Media Technology, School of Creative Media, City University of Hong Kong, Hong Kong, China. From 2006 to 2007, he was a Postdoctoral Fellow with the Human-Computer Communications Laboratory (HCCL), The Chinese University of Hong Kong, Hong Kong, China. He is currently a Professor with School of Computer Science, Northwestern Polytechnical University, Xian, China and leads the Audio, Speech and Language Processing Group (ASLP@NPU). He has published over 200 papers in referred journals and conferences, such as IEEE/ACM Transactions on Audio, Speech and Language Processing, IEEE Transactions on Multimedia, Interspeech, ICASSP, ASRU, ACL and ACM Multimedia. He has achieved several best paper awards in flagship conferences. His current research interests include general topics in speech and language processing, multimedia, and human-computer interaction. Dr. Xie is currently an associate editor (AE) of IEEE/ACM Trans. on Audio, Speech and language Processing. He has actively served as Chairs in many conferences and technical committees. He serves as an IEEE Speech and Language Processing
-Technical Committee Member.
 
 ***Kong Aik Lee, Senior Scientist at Institute for Infocomm Research, A\*Star, Singapore***
 
@@ -14,55 +12,37 @@ Email: [kongaik.lee@ieee.org](mailto:kongaik.lee@ieee.org)
 
 <img src="images/kong.png" alt="kong" width="20%">
 
-Kong Aik Lee started off him career as a researcher, then a team leader and a  strategic planning manager, at the Institute Infocomm Research, A*STAR, Singapore, working on speaker and language recognition research. From 2018 to 2020, he spent two and a half years in NEC Corporation, Japan, focusing very much on voice biometrics and multi-modal biometrics products.  He is proud to work with a great team on voice biometrics featured on NEC Bio-Idiom platform. He returned to Singapore in July 2020, and now leading the speech and audio analytics research at the Institute for Infocomm Research, as a Senior Scientist and PI.  He also serve as an Editor for Elsevier Computer Speech and Language (since 2016), and was an Associate Editor for IEEE/ACM Transactions on Audio, Speech and Language Processing (2017 - 2021), and am an elected member of IEEE Speech and Language Technical Committee (2019 - 2021).
 
 ***Zhijie Yan, Principal Engineer at Alibaba, China***
 Email: [zhijie.yzj@alibaba-inc.com](mailto:zhijie.yzj@alibaba-inc.com)
 
 <img src="images/zhijie.jpg" alt="zhijie" width="20%">
 
-Zhijie Yan holds a PhD from the University of Science and Technology of China, and is a senior member of the Institute of Electrical and Electronics Engineers (IEEE). He is also an expert reviewer of top academic conferences and journals in the speech field. His research fields include speech recognition, speech synthesis, voiceprints, and speech interaction. His research results are applied in speech services provided by Alibaba Group and Ant Financial. He was awarded the title of "One of the Top 100 Grassroots Scientists" by the China Association for Science and Technology.
-
-
 ***Shiliang Zhang, Senior Engineer at Alibaba, China***
 Email: [sly.zsl@alibaba-inc.com](mailto:sly.zsl@alibaba-inc.com)
 
 <img src="images/zsl.JPG" alt="zsl" width="20%">
 
-Shiliang Zhang graduated with a Ph.D. from the University of Science and Technology of China in 2017. His research areas mainly include speech recognition, natural language understanding, and machine learning. Currently, he has published over 40 papers in mainstream academic journals and conferences in the fields of speech and machine learning, and has applied for dozens of patents. After obtaining his doctorate degree, he joined the Alibaba Intelligent Speech team. He is currently leading the direction of speech recognition and fundamental technology at DAMO Academy's speech laboratory.
-
-
-
 ***Yanmin Qian, Professor, Shanghai Jiao Tong University, China***
 
 Email: [yanminqian@sjtu.edu.cn](mailto:yanminqian@sjtu.edu.cn)
 
 <img src="images/qian.jpeg" alt="qian" width="20%">
 
-Yanmin Qian received the B.S. degree from the Department of Electronic and Information Engineering,Huazhong University of Science and Technology, Wuhan, China, in 2007, and the Ph.D. degree from the Department of Electronic Engineering, Tsinghua University, Beijing, China, in 2012. Since 2013, he has been with the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), Shanghai, China, where he is currently an Associate Professor. From 2015 to 2016, he also worked as an Associate Research in the Speech Group, Cambridge University Engineering Department, Cambridge, U.K. He is a senior member of IEEE and a member of ISCA, and one of the founding members of Kaldi Speech Recognition Toolkit. He has published more than 110 papers on speech and language processing with 4000+ citations, including the top conference: ICASSP, INTERSPEECH and ASRU. His current research interests include the acoustic and language modeling in speech recognition, speaker and language recognition, key word spotting, and multimedia signal processing.
-
-
-
 ***Zhuo Chen, Applied Scientist in Microsoft, USA***
 
 Email: [zhuc@microsoft.com](mailto:zhuc@microsoft.com)
 
 <img src="images/chenzhuo.jpg" alt="chenzhuo" width="20%">
 
-Zhuo Chen received the Ph.D. degree from Columbia University, New York, NY, USA, in 2017. He is currently a Principal Applied Data Scientist with Microsoft. He has authored or coauthored more than 80 papers in peer-reviewed journals and conferences with around 6000 citations. He is a reviewer or technical committee member for more than ten journals and conferences. His research interests include automatic conversation recognition, speech separation, diarisation, and speaker information extraction. He actively participated in the academic events and challenges, and won several awards. Meanwhile, he contributed to open-sourced datasets, such as WSJ0-2mix, LibriCSS, and AISHELL-4, that have been main benchmark datasets for multi-speaker processing research. In 2020, he was the Team Leader in 2020 Jelinek workshop, leading more than 30 researchers and students to push the state of the art in conversation transcription.
-
 ***Jian Wu, Applied Scientist in Microsoft, USA***
 
 Email: [wujian@microsoft.com](mailto:wujian@microsoft.com)
 
 <img src="images/wujian.jpg" alt="wujian" width="20%">
 
-Jian Wu received a master degree from Northwestern Polytechnical University, Xi'an, China, in 2020 and currently he is a Applied Scientist in Microsoft, USA. His research interests cover multi-channel signal processing, robust and multi-talker speech recognition, speech enhancement, dereverberation and separation. He has around 30 conference publications with a total citation over 1200. He participated in several challenges such as CHiME5, DNS 2020 and FFSVC 2020 and contributed to the open-sourced datasets including LibriCSS and AISHELL-4. He is also a reviewer for several journals and conferences such as ICASSP, SLT, TASLP and SPL.
-
 ***Hui Bu, CEO, AISHELL foundation, China***
 
 Email: [buhui@aishelldata.com](mailto:buhui@aishelldata.com)
 
 <img src="images/buhui.jpeg" alt="buhui" width="20%">
-
-Hui Bu received his master degree in the Artificial Intelligence Laboratory of Korea University in 2014. He is the founder and the CEO of AISHELL and AISHELL foundation. He participated in the release of AISHELL 1 \& 2 \& 3 \& 4, DMASH and HI-MIA open source database project and is the co-founder of China Kaldi offline Technology Forum.
diff --git a/docs_m2met2/_build/html/_sources/index.rst.txt b/docs_m2met2/_build/html/_sources/index.rst.txt
index 04cb9345d..e0e7562c7 100644
--- a/docs_m2met2/_build/html/_sources/index.rst.txt
+++ b/docs_m2met2/_build/html/_sources/index.rst.txt
@@ -20,10 +20,3 @@ To facilitate reproducible research, we provide a comprehensive overview of the
    ./Rules
    ./Organizers
    ./Contact
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
\ No newline at end of file
diff --git a/docs_m2met2/_build/html/index.html b/docs_m2met2/_build/html/index.html
index a65dfa0cb..acd0d32c4 100644
--- a/docs_m2met2/_build/html/index.html
+++ b/docs_m2met2/_build/html/index.html
@@ -135,14 +135,6 @@ To facilitate reproducible research, we provide a comprehensive overview of the
 <li class="toctree-l1"><a class="reference internal" href="Contact.html">Contact</a></li>
 </ul>
 </div>
-</section>
-<section id="indices-and-tables">
-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this heading">¶</a></h1>
-<ul class="simple">
-<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
-<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
-<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
-</ul>
 </section>
 
 
diff --git a/docs_m2met2/_build/html/searchindex.js b/docs_m2met2/_build/html/searchindex.js
index 940cc2856..1542f8e9c 100644
--- a/docs_m2met2/_build/html/searchindex.js
+++ b/docs_m2met2/_build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs_m2met2/index.rst b/docs_m2met2/index.rst
index 04cb9345d..e0e7562c7 100644
--- a/docs_m2met2/index.rst
+++ b/docs_m2met2/index.rst
@@ -20,10 +20,3 @@ To facilitate reproducible research, we provide a comprehensive overview of the
    ./Rules
    ./Organizers
    ./Contact
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
\ No newline at end of file
diff --git a/docs_m2met2_cn/_build/doctrees/environment.pickle b/docs_m2met2_cn/_build/doctrees/environment.pickle
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diff --git a/docs_m2met2_cn/_build/doctrees/index.doctree b/docs_m2met2_cn/_build/doctrees/index.doctree
index c816bd9889b5857ae508815fded99bd93c021832..1ba5a56057594c4b718bb4d01b49b8d995a90b55 100644
GIT binary patch
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diff --git a/docs_m2met2_cn/_build/html/_sources/index.rst.txt b/docs_m2met2_cn/_build/html/_sources/index.rst.txt
index b35ac96da..c089b36e7 100644
--- a/docs_m2met2_cn/_build/html/_sources/index.rst.txt
+++ b/docs_m2met2_cn/_build/html/_sources/index.rst.txt
@@ -20,10 +20,3 @@ ASRU 2023 多通道多方会议转录挑战 2.0
    ./规则
    ./组委会
    ./联系方式
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/docs_m2met2_cn/_build/html/index.html b/docs_m2met2_cn/_build/html/index.html
index 4bfbdbfd9..5e92ef3d7 100644
--- a/docs_m2met2_cn/_build/html/index.html
+++ b/docs_m2met2_cn/_build/html/index.html
@@ -136,14 +136,6 @@
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 </ul>
 </div>
-</section>
-<section id="indices-and-tables">
-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="此标题的永久链接">¶</a></h1>
-<ul class="simple">
-<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">索引</span></a></p></li>
-<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">模块索引</span></a></p></li>
-<li><p><a class="reference internal" href="search.html"><span class="std std-ref">搜索页面</span></a></p></li>
-</ul>
 </section>
 
 
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index 8042b77c9..5a84a48fe 100644
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diff --git a/docs_m2met2_cn/_build/html/基线.html b/docs_m2met2_cn/_build/html/基线.html
index 569ba57e7..322a26197 100644
--- a/docs_m2met2_cn/_build/html/基线.html
+++ b/docs_m2met2_cn/_build/html/基线.html
@@ -102,7 +102,7 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%A7%84%E5%88%99.html">竞赛规则</a></li>
 <li class="toctree-l1"><a class="reference internal" href="%E7%BB%84%E5%A7%94%E4%BC%9A.html">组委会</a></li>
-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">联系方式</a></li>
 </ul>
 
     
diff --git a/docs_m2met2_cn/_build/html/数据集.html b/docs_m2met2_cn/_build/html/数据集.html
index 3ceb89cfa..276f7b76e 100644
--- a/docs_m2met2_cn/_build/html/数据集.html
+++ b/docs_m2met2_cn/_build/html/数据集.html
@@ -102,7 +102,7 @@
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%A7%84%E5%88%99.html">竞赛规则</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">联系方式</a></li>
 </ul>
 
     
diff --git a/docs_m2met2_cn/_build/html/简介.html b/docs_m2met2_cn/_build/html/简介.html
index 00e916c96..a75969a41 100644
--- a/docs_m2met2_cn/_build/html/简介.html
+++ b/docs_m2met2_cn/_build/html/简介.html
@@ -103,7 +103,7 @@
 </li>
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-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">联系方式</a></li>
 </ul>
 
     
diff --git a/docs_m2met2_cn/_build/html/组委会.html b/docs_m2met2_cn/_build/html/组委会.html
index c295c28ec..3fac9f690 100644
--- a/docs_m2met2_cn/_build/html/组委会.html
+++ b/docs_m2met2_cn/_build/html/组委会.html
@@ -27,7 +27,7 @@
     <script src="_static/translations.js"></script>
     <link rel="index" title="索引" href="genindex.html" />
     <link rel="search" title="搜索" href="search.html" />
-    <link rel="next" title="Contact" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" />
+    <link rel="next" title="联系方式" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" />
     <link rel="prev" title="竞赛规则" href="%E8%A7%84%E5%88%99.html" />
   
    
@@ -40,7 +40,7 @@
           <a href="genindex.html" title="总索引"
              accesskey="I">索引</a></li>
         <li class="right" >
-          <a href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="Contact"
+          <a href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="联系方式"
              accesskey="N">下一页</a> |</li>
         <li class="right" >
           <a href="%E8%A7%84%E5%88%99.html" title="竞赛规则"
@@ -102,7 +102,7 @@
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-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">联系方式</a></li>
 </ul>
 
     
@@ -168,7 +168,7 @@
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       <div class="pull-right">
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+        <a class="btn btn-default" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="下一章 (use the right arrow)">联系方式</a>
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@@ -183,7 +183,7 @@
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         <li class="right" >
-          <a href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="Contact"
+          <a href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html" title="联系方式"
              >下一页</a> |</li>
         <li class="right" >
           <a href="%E8%A7%84%E5%88%99.html" title="竞赛规则"
diff --git a/docs_m2met2_cn/_build/html/规则.html b/docs_m2met2_cn/_build/html/规则.html
index b6fae2901..6a66fd730 100644
--- a/docs_m2met2_cn/_build/html/规则.html
+++ b/docs_m2met2_cn/_build/html/规则.html
@@ -102,7 +102,7 @@
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-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">联系方式</a></li>
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diff --git a/docs_m2met2_cn/_build/html/赛道设置与评估.html b/docs_m2met2_cn/_build/html/赛道设置与评估.html
index 7df9889df..b9876b438 100644
--- a/docs_m2met2_cn/_build/html/赛道设置与评估.html
+++ b/docs_m2met2_cn/_build/html/赛道设置与评估.html
@@ -103,7 +103,7 @@
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-<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">Contact</a></li>
+<li class="toctree-l1"><a class="reference internal" href="%E8%81%94%E7%B3%BB%E6%96%B9%E5%BC%8F.html">联系方式</a></li>
 </ul>
 
     
diff --git a/docs_m2met2_cn/index.rst b/docs_m2met2_cn/index.rst
index b35ac96da..c089b36e7 100644
--- a/docs_m2met2_cn/index.rst
+++ b/docs_m2met2_cn/index.rst
@@ -20,10 +20,3 @@ ASRU 2023 多通道多方会议转录挑战 2.0
    ./规则
    ./组委会
    ./联系方式
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`

From 40f1f529b0ebc7a571af6be3ca9df67cd5480d66 Mon Sep 17 00:00:00 2001
From: yhliang <429259365@qq.com>
Date: Thu, 20 Apr 2023 16:48:26 +0800
Subject: [PATCH 2/4] add m2met2 registration form

---
 docs_m2met2/Dataset.md                        |   2 +-
 docs_m2met2/Introduction.md                   |   4 +++-
 docs_m2met2/Track_setting_and_evaluation.md   |   4 ++--
 docs_m2met2/_build/doctrees/Dataset.doctree   | Bin 14369 -> 14409 bytes
 .../_build/doctrees/Introduction.doctree      | Bin 14733 -> 15176 bytes
 .../Track_setting_and_evaluation.doctree      | Bin 12729 -> 12655 bytes
 .../_build/doctrees/environment.pickle        | Bin 25953 -> 25894 bytes
 docs_m2met2/_build/html/Baseline.html         |   2 +-
 docs_m2met2/_build/html/Contact.html          |   2 +-
 docs_m2met2/_build/html/Dataset.html          |   4 ++--
 docs_m2met2/_build/html/Introduction.html     |   5 +++--
 docs_m2met2/_build/html/Organizers.html       |   2 +-
 docs_m2met2/_build/html/Rules.html            |   2 +-
 .../html/Track_setting_and_evaluation.html    |   8 ++++----
 .../_build/html/_sources/Dataset.md.txt       |   2 +-
 .../_build/html/_sources/Introduction.md.txt  |   4 +++-
 .../Track_setting_and_evaluation.md.txt       |   4 ++--
 docs_m2met2/_build/html/genindex.html         |   2 +-
 docs_m2met2/_build/html/index.html            |   2 +-
 docs_m2met2/_build/html/search.html           |   2 +-
 docs_m2met2/_build/html/searchindex.js        |   2 +-
 .../_build/doctrees/environment.pickle        | Bin 25645 -> 25266 bytes
 docs_m2met2_cn/_build/doctrees/数据集.doctree | Bin 12226 -> 12277 bytes
 docs_m2met2_cn/_build/doctrees/简介.doctree   | Bin 12422 -> 12888 bytes
 .../_build/doctrees/赛道设置与评估.doctree    | Bin 11464 -> 11286 bytes
 .../_build/html/_sources/数据集.md.txt        |   4 ++--
 .../_build/html/_sources/简介.md.txt          |   2 ++
 .../html/_sources/赛道设置与评估.md.txt       |   6 +++---
 docs_m2met2_cn/_build/html/genindex.html      |   2 +-
 docs_m2met2_cn/_build/html/index.html         |   2 +-
 docs_m2met2_cn/_build/html/search.html        |   2 +-
 docs_m2met2_cn/_build/html/searchindex.js     |   2 +-
 docs_m2met2_cn/_build/html/基线.html          |   2 +-
 docs_m2met2_cn/_build/html/数据集.html        |   6 +++---
 docs_m2met2_cn/_build/html/简介.html          |   3 ++-
 docs_m2met2_cn/_build/html/组委会.html        |   2 +-
 docs_m2met2_cn/_build/html/联系方式.html      |   2 +-
 docs_m2met2_cn/_build/html/规则.html          |   2 +-
 .../_build/html/赛道设置与评估.html           |   8 ++++----
 docs_m2met2_cn/数据集.md                      |   4 ++--
 docs_m2met2_cn/简介.md                        |   2 ++
 docs_m2met2_cn/赛道设置与评估.md              |   6 +++---
 42 files changed, 60 insertions(+), 50 deletions(-)

diff --git a/docs_m2met2/Dataset.md b/docs_m2met2/Dataset.md
index c987acac9..a897d673b 100644
--- a/docs_m2met2/Dataset.md
+++ b/docs_m2met2/Dataset.md
@@ -2,7 +2,7 @@
 ## Overview of training data
 In the fixed training condition, the training dataset is restricted to three publicly available corpora, namely, AliMeeting, AISHELL-4, and CN-Celeb. To evaluate the performance of the models trained on these datasets, we will release a new Test set called Test-2023 for scoring and ranking. We will describe the AliMeeting dataset and the Test-2023 set in detail.
 ## Detail of AliMeeting corpus
-AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train and Eval sets contain 212 and 8 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train and Eval sets is 456 and 25, respectively, with balanced gender coverage.
+AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train, Eval and Test sets contain 212, 8 and 20 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train, Eval and Test sets is 456, 25 and 60, respectively, with balanced gender coverage.
 
 The dataset is collected in 13 meeting venues, which are categorized into three types: small, medium, and large rooms with sizes ranging from 8 m$^{2}$ to 55 m$^{2}$. Different rooms give us a variety of acoustic properties and layouts. The detailed parameters of each meeting venue will be released together with the Train data. The type of wall material of the meeting venues covers cement, glass, etc. Other furnishings in meeting venues include sofa, TV, blackboard, fan, air conditioner, plants, etc. During recording, the participants of the meeting sit around the microphone array which is placed on the table and conduct a natural conversation. The microphone-speaker distance ranges from 0.3 m to 5.0 m. All participants are native Chinese speakers speaking Mandarin without strong accents. During the meeting, various kinds of indoor noise including but not limited to clicking, keyboard, door opening/closing, fan, bubble noise, etc., are made naturally. For both Train and Eval sets, the participants are required to remain in the same position during recording. There is no speaker overlap between the Train and Eval set. An example of the recording venue from the Train set is shown in Fig 1.
 
diff --git a/docs_m2met2/Introduction.md b/docs_m2met2/Introduction.md
index e1f9fc792..b7f783ebb 100644
--- a/docs_m2met2/Introduction.md
+++ b/docs_m2met2/Introduction.md
@@ -20,6 +20,8 @@ Building on the success of the previous M2MeT challenge, we are excited to propo
 
 ## Guidelines
 
-Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023. 
+Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 5, 2023. 
+
+[M2MET2.0 Registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
 
 Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top three submissions to be included in the ASRU2023 Proceedings. 
diff --git a/docs_m2met2/Track_setting_and_evaluation.md b/docs_m2met2/Track_setting_and_evaluation.md
index b90c17af1..2b75fcac5 100644
--- a/docs_m2met2/Track_setting_and_evaluation.md
+++ b/docs_m2met2/Track_setting_and_evaluation.md
@@ -1,6 +1,6 @@
 # Track & Evaluation 
-## Speaker-Attributed ASR (Main Track)
-The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps. Instead, segments containing multiple speakers will be provided on the Test-2023 set, which can be obtained using a simple voice activity detection (VAD) model.
+## Speaker-Attributed ASR
+The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps of the Test-2023 set. Instead, segments containing multiple speakers will be provided, which can be obtained using a simple voice activity detection (VAD) model.
 
 ![task difference](images/task_diff.png)
 
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diff --git a/docs_m2met2/_build/html/Baseline.html b/docs_m2met2/_build/html/Baseline.html
index 8893a1692..e52d32275 100644
--- a/docs_m2met2/_build/html/Baseline.html
+++ b/docs_m2met2/_build/html/Baseline.html
@@ -88,7 +88,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
diff --git a/docs_m2met2/_build/html/Contact.html b/docs_m2met2/_build/html/Contact.html
index 44546d590..eafd2d5b8 100644
--- a/docs_m2met2/_build/html/Contact.html
+++ b/docs_m2met2/_build/html/Contact.html
@@ -84,7 +84,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
diff --git a/docs_m2met2/_build/html/Dataset.html b/docs_m2met2/_build/html/Dataset.html
index 88531f6d0..43bf8a121 100644
--- a/docs_m2met2/_build/html/Dataset.html
+++ b/docs_m2met2/_build/html/Dataset.html
@@ -89,7 +89,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
@@ -131,7 +131,7 @@
 </section>
 <section id="detail-of-alimeeting-corpus">
 <h2>Detail of AliMeeting corpus<a class="headerlink" href="#detail-of-alimeeting-corpus" title="Permalink to this heading">¶</a></h2>
-<p>AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train and Eval sets contain 212 and 8 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train and Eval sets is 456 and 25, respectively, with balanced gender coverage.</p>
+<p>AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train, Eval and Test sets contain 212, 8 and 20 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train, Eval and Test sets is 456, 25 and 60, respectively, with balanced gender coverage.</p>
 <p>The dataset is collected in 13 meeting venues, which are categorized into three types: small, medium, and large rooms with sizes ranging from 8 m<span class="math notranslate nohighlight">\(^{2}\)</span> to 55 m<span class="math notranslate nohighlight">\(^{2}\)</span>. Different rooms give us a variety of acoustic properties and layouts. The detailed parameters of each meeting venue will be released together with the Train data. The type of wall material of the meeting venues covers cement, glass, etc. Other furnishings in meeting venues include sofa, TV, blackboard, fan, air conditioner, plants, etc. During recording, the participants of the meeting sit around the microphone array which is placed on the table and conduct a natural conversation. The microphone-speaker distance ranges from 0.3 m to 5.0 m. All participants are native Chinese speakers speaking Mandarin without strong accents. During the meeting, various kinds of indoor noise including but not limited to clicking, keyboard, door opening/closing, fan, bubble noise, etc., are made naturally. For both Train and Eval sets, the participants are required to remain in the same position during recording. There is no speaker overlap between the Train and Eval set. An example of the recording venue from the Train set is shown in Fig 1.</p>
 <p><img alt="meeting room" src="_images/meeting_room.png" /></p>
 <p>The number of participants within one meeting session ranges from 2 to 4. To ensure the coverage of different overlap ratios, we select various meeting topics during recording, including medical treatment, education, business, organization management, industrial production and other daily routine meetings. The average speech overlap ratio of Train, Eval and Test sets are 42.27%, 34.76% and 42.8%, respectively. More details of AliMeeting are shown in Table 1. A detailed overlap ratio distribution of meeting sessions with different numbers of speakers in the Train, Eval and Test set is shown in Table 2.</p>
diff --git a/docs_m2met2/_build/html/Introduction.html b/docs_m2met2/_build/html/Introduction.html
index d75fa954e..a1950685f 100644
--- a/docs_m2met2/_build/html/Introduction.html
+++ b/docs_m2met2/_build/html/Introduction.html
@@ -89,7 +89,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
@@ -146,7 +146,8 @@
 </section>
 <section id="guidelines">
 <h2>Guidelines<a class="headerlink" href="#guidelines" title="Permalink to this heading">¶</a></h2>
-<p>Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023.</p>
+<p>Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 5, 2023.</p>
+<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MET2.0 registration</a></p>
 <p>Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top three submissions to be included in the ASRU2023 Proceedings.</p>
 </section>
 </section>
diff --git a/docs_m2met2/_build/html/Organizers.html b/docs_m2met2/_build/html/Organizers.html
index f66c037f9..0a8811e78 100644
--- a/docs_m2met2/_build/html/Organizers.html
+++ b/docs_m2met2/_build/html/Organizers.html
@@ -88,7 +88,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
diff --git a/docs_m2met2/_build/html/Rules.html b/docs_m2met2/_build/html/Rules.html
index 3d2bddebe..8eef8aad7 100644
--- a/docs_m2met2/_build/html/Rules.html
+++ b/docs_m2met2/_build/html/Rules.html
@@ -88,7 +88,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
diff --git a/docs_m2met2/_build/html/Track_setting_and_evaluation.html b/docs_m2met2/_build/html/Track_setting_and_evaluation.html
index f377ee5ea..859f4444a 100644
--- a/docs_m2met2/_build/html/Track_setting_and_evaluation.html
+++ b/docs_m2met2/_build/html/Track_setting_and_evaluation.html
@@ -89,7 +89,7 @@
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
@@ -125,9 +125,9 @@
             
   <section id="track-evaluation">
 <h1>Track &amp; Evaluation<a class="headerlink" href="#track-evaluation" title="Permalink to this heading">¶</a></h1>
-<section id="speaker-attributed-asr-main-track">
-<h2>Speaker-Attributed ASR (Main Track)<a class="headerlink" href="#speaker-attributed-asr-main-track" title="Permalink to this heading">¶</a></h2>
-<p>The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It’s worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps. Instead, segments containing multiple speakers will be provided on the Test-2023 set, which can be obtained using a simple voice activity detection (VAD) model.</p>
+<section id="speaker-attributed-asr">
+<h2>Speaker-Attributed ASR<a class="headerlink" href="#speaker-attributed-asr" title="Permalink to this heading">¶</a></h2>
+<p>The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It’s worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps of the Test-2023 set. Instead, segments containing multiple speakers will be provided, which can be obtained using a simple voice activity detection (VAD) model.</p>
 <p><img alt="task difference" src="_images/task_diff.png" /></p>
 </section>
 <section id="evaluation-metric">
diff --git a/docs_m2met2/_build/html/_sources/Dataset.md.txt b/docs_m2met2/_build/html/_sources/Dataset.md.txt
index c987acac9..a897d673b 100644
--- a/docs_m2met2/_build/html/_sources/Dataset.md.txt
+++ b/docs_m2met2/_build/html/_sources/Dataset.md.txt
@@ -2,7 +2,7 @@
 ## Overview of training data
 In the fixed training condition, the training dataset is restricted to three publicly available corpora, namely, AliMeeting, AISHELL-4, and CN-Celeb. To evaluate the performance of the models trained on these datasets, we will release a new Test set called Test-2023 for scoring and ranking. We will describe the AliMeeting dataset and the Test-2023 set in detail.
 ## Detail of AliMeeting corpus
-AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train and Eval sets contain 212 and 8 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train and Eval sets is 456 and 25, respectively, with balanced gender coverage.
+AliMeeting contains 118.75 hours of speech data in total. The dataset is divided into 104.75 hours for training (Train), 4 hours for evaluation (Eval) and 10 hours as test set (Test) for scoring and ranking. Specifically, the Train, Eval and Test sets contain 212, 8 and 20 sessions, respectively. Each session consists of a 15 to 30-minute discussion by a group of participants. The total number of participants in Train, Eval and Test sets is 456, 25 and 60, respectively, with balanced gender coverage.
 
 The dataset is collected in 13 meeting venues, which are categorized into three types: small, medium, and large rooms with sizes ranging from 8 m$^{2}$ to 55 m$^{2}$. Different rooms give us a variety of acoustic properties and layouts. The detailed parameters of each meeting venue will be released together with the Train data. The type of wall material of the meeting venues covers cement, glass, etc. Other furnishings in meeting venues include sofa, TV, blackboard, fan, air conditioner, plants, etc. During recording, the participants of the meeting sit around the microphone array which is placed on the table and conduct a natural conversation. The microphone-speaker distance ranges from 0.3 m to 5.0 m. All participants are native Chinese speakers speaking Mandarin without strong accents. During the meeting, various kinds of indoor noise including but not limited to clicking, keyboard, door opening/closing, fan, bubble noise, etc., are made naturally. For both Train and Eval sets, the participants are required to remain in the same position during recording. There is no speaker overlap between the Train and Eval set. An example of the recording venue from the Train set is shown in Fig 1.
 
diff --git a/docs_m2met2/_build/html/_sources/Introduction.md.txt b/docs_m2met2/_build/html/_sources/Introduction.md.txt
index e1f9fc792..27322dece 100644
--- a/docs_m2met2/_build/html/_sources/Introduction.md.txt
+++ b/docs_m2met2/_build/html/_sources/Introduction.md.txt
@@ -20,6 +20,8 @@ Building on the success of the previous M2MeT challenge, we are excited to propo
 
 ## Guidelines
 
-Interested participants, whether from academia or industry, must register for the challenge by completing a Google form, which will be available here. The deadline for registration is May 5, 2023. 
+Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 5, 2023. 
+
+[M2MET2.0 registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
 
 Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top three submissions to be included in the ASRU2023 Proceedings. 
diff --git a/docs_m2met2/_build/html/_sources/Track_setting_and_evaluation.md.txt b/docs_m2met2/_build/html/_sources/Track_setting_and_evaluation.md.txt
index b90c17af1..2b75fcac5 100644
--- a/docs_m2met2/_build/html/_sources/Track_setting_and_evaluation.md.txt
+++ b/docs_m2met2/_build/html/_sources/Track_setting_and_evaluation.md.txt
@@ -1,6 +1,6 @@
 # Track & Evaluation 
-## Speaker-Attributed ASR (Main Track)
-The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps. Instead, segments containing multiple speakers will be provided on the Test-2023 set, which can be obtained using a simple voice activity detection (VAD) model.
+## Speaker-Attributed ASR
+The speaker-attributed ASR task poses a unique challenge of transcribing speech from multiple speakers and assigning a speaker label to the transcription. Figure 2 illustrates the difference between the speaker-attributed ASR task and the multi-speaker ASR task. This track allows for the use of the AliMeeting, Aishell4, and Cn-Celeb datasets as constrained data sources during both training and evaluation. The AliMeeting dataset, which was used in the M2MeT challenge, includes Train, Eval, and Test sets. Additionally, a new Test-2023 set, consisting of approximately 10 hours of meeting data recorded in an identical acoustic setting as the AliMeeting corpus, will be released soon for challenge scoring and ranking. It's worth noting that the organizers will not provide the near-field audio, transcriptions, or oracle timestamps of the Test-2023 set. Instead, segments containing multiple speakers will be provided, which can be obtained using a simple voice activity detection (VAD) model.
 
 ![task difference](images/task_diff.png)
 
diff --git a/docs_m2met2/_build/html/genindex.html b/docs_m2met2/_build/html/genindex.html
index 0071a1845..e7e17b6ed 100644
--- a/docs_m2met2/_build/html/genindex.html
+++ b/docs_m2met2/_build/html/genindex.html
@@ -79,7 +79,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
diff --git a/docs_m2met2/_build/html/index.html b/docs_m2met2/_build/html/index.html
index acd0d32c4..dcbb8cb03 100644
--- a/docs_m2met2/_build/html/index.html
+++ b/docs_m2met2/_build/html/index.html
@@ -84,7 +84,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
diff --git a/docs_m2met2/_build/html/search.html b/docs_m2met2/_build/html/search.html
index 344ad958a..71adf366d 100644
--- a/docs_m2met2/_build/html/search.html
+++ b/docs_m2met2/_build/html/search.html
@@ -72,7 +72,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="Track_setting_and_evaluation.html">Track &amp; Evaluation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr-main-track">Speaker-Attributed ASR (Main Track)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#speaker-attributed-asr">Speaker-Attributed ASR</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#evaluation-metric">Evaluation metric</a></li>
 <li class="toctree-l2"><a class="reference internal" href="Track_setting_and_evaluation.html#sub-track-arrangement">Sub-track arrangement</a></li>
 </ul>
diff --git a/docs_m2met2/_build/html/searchindex.js b/docs_m2met2/_build/html/searchindex.js
index 1542f8e9c..6fd42560b 100644
--- a/docs_m2met2/_build/html/searchindex.js
+++ b/docs_m2met2/_build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["Baseline", "Contact", "Dataset", "Introduction", "Organizers", "Rules", "Track_setting_and_evaluation", "index"], "filenames": ["Baseline.md", "Contact.md", "Dataset.md", "Introduction.md", "Organizers.md", "Rules.md", "Track_setting_and_evaluation.md", "index.rst"], "titles": ["Baseline", "Contact", "Datasets", "Introduction", "Organizers", "Rules", "Track &amp; Evaluation", "ASRU 2023 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE 2.0 (M2MeT2.0)"], "terms": {"we": [0, 2, 3, 7], "releas": [0, 2, 3, 6], "an": [0, 2, 3, 6], "e2": 0, "sa": 0, "asr": [0, 3, 7], "cite": 0, "kanda21b_interspeech": 0, "conduct": [0, 2], "funasr": 0, "time": [0, 6], "accord": [0, 3], "timelin": [0, 2], "The": [0, 2, 3, 5, 6], "model": [0, 2, 3, 5, 6], "architectur": 0, "i": [0, 2, 3, 5], "shown": [0, 2], "figur": [0, 6], "3": [0, 2, 3], "speakerencod": 0, "initi": 0, "pre": [0, 6], "train": [0, 3, 5, 7], "speaker": [0, 2, 3, 7], "verif": 0, "from": [0, 2, 3, 5, 6], 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diff --git a/docs_m2met2_cn/_build/doctrees/数据集.doctree b/docs_m2met2_cn/_build/doctrees/数据集.doctree
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diff --git a/docs_m2met2_cn/_build/html/_sources/数据集.md.txt b/docs_m2met2_cn/_build/html/_sources/数据集.md.txt
index 52965a1c5..24bfaf3d9 100644
--- a/docs_m2met2_cn/_build/html/_sources/数据集.md.txt
+++ b/docs_m2met2_cn/_build/html/_sources/数据集.md.txt
@@ -3,13 +3,13 @@
 在限定数据集条件下，训练数据集仅限于三个公开的语料库，即AliMeeting、AISHELL-4和CN-Celeb。为了评估参赛者提交的模型的性能，我们将发布一个新的测试集（Test-2023）用于打分和排名。下面我们将详细描述AliMeeting数据集和Test-2023测试集。
 
 ## Alimeeting数据集介绍
-AliMeeting总共包含118.75小时的语音数据，包括104.75小时的训练集（Train）、4小时的验证集（Eval）和10小时的测试集（Test）。Train集和Eval集分别包含212场和8场会议，其中每场会议由多个说话人进行15到30分钟的讨论。Train和Eval集中参与会议的总人数分别为456人和25人，并且参会的男女比例人数均衡。
+AliMeeting总共包含118.75小时的语音数据，包括104.75小时的训练集（Train）、4小时的验证集（Eval）和10小时的测试集（Test）。Train集，Eval集和Test集分别包含212场和8场会议，其中每场会议由多个说话人进行15到30分钟的讨论。Train，Eval和Test集中参与会议的总人数分别为456人和25人，并且参会的男女比例人数均衡。
 
 该数据集收集于13个不同的会议室，按照大小规格分为小型、中型和大型三种，房间面积从8到55平方米不等。不同房间具有不同的布局和声学特性，每个房间的详细参数也将发送给参与者。会议场地的墙体材料类型包括水泥、玻璃等。会议场地的家具包括沙发、电视、黑板、风扇、空调、植物等。在录制过程中，麦克风阵列放置于桌上，多个说话人围坐在桌边进行自然对话。麦克风阵列离说话人距离约0.3到5.0米之间。所有说话人的母语均是汉语，并且说的都是普通话，没有浓重的口音。在会议录制期间可能会产生各种室内的噪音，包括键盘声、开门/关门声、风扇声、气泡声等。所有说话人在会议的录制期间均保持相同位置，不发生走动。训练集和验证集的说话人没有重复。图1展示了一个会议室的布局以及麦克风的拓扑结构。
 
 ![meeting room](images/meeting_room.png)
 
-每场会议的说话人数量从2到4人不等。同时为了覆盖各种内容的会议场景，我们选择了多种会议主题，包括医疗、教育、商业、组织管理、工业生产等不同内容的例会。Train集，Eval集和Test集的平均语音重叠率分别为42.27\%和34.76\%。AliMeeting Train集，Eval集和Test集的详细信息见表1。表2显示了Train集,Eval集和Test集中不同发言者人数会议的语音重叠率和会议数量。
+每场会议的说话人数量从2到4人不等。同时为了覆盖各种内容的会议场景，我们选择了多种会议主题，包括医疗、教育、商业、组织管理、工业生产等不同内容的例会。Train集，Eval集和Test集的平均语音重叠率分别为42.27\%，34.76\%和42.8\%。AliMeeting Train集，Eval集和Test集的详细信息见表1。表2显示了Train集,Eval集和Test集中不同发言者人数会议的语音重叠率和会议数量。
 
 ![dataset detail](images/dataset_detail.png)
 Test-2023测试集由20场会议组成，这些会议是在与AliMeeting数据集相同的声学环境下录制的。Test-2023测试集中的每个会议环节由2到4个参与者组成并且与AliMeeting测试集的配置相似。
diff --git a/docs_m2met2_cn/_build/html/_sources/简介.md.txt b/docs_m2met2_cn/_build/html/_sources/简介.md.txt
index cf4dfb86a..4ae9b6759 100644
--- a/docs_m2met2_cn/_build/html/_sources/简介.md.txt
+++ b/docs_m2met2_cn/_build/html/_sources/简介.md.txt
@@ -25,4 +25,6 @@ ASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说话
 
 来自学术界和工业界的有意向参赛者均应在2023年5月5日及之前填写下方的谷歌表单：
 
+[M2MET2.0报名](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
+
 主办方将在3个工作日内通过电子邮件通知符合条件的参赛团队，团队必须遵守将在挑战网站上发布的挑战规则。在排名发布之前，每个参赛者必须提交一份系统描述文件，详细说明使用的方法和模型。主办方将选择前三名纳入ASRU2023论文集。
\ No newline at end of file
diff --git a/docs_m2met2_cn/_build/html/_sources/赛道设置与评估.md.txt b/docs_m2met2_cn/_build/html/_sources/赛道设置与评估.md.txt
index a99c1eddc..94a623690 100644
--- a/docs_m2met2_cn/_build/html/_sources/赛道设置与评估.md.txt
+++ b/docs_m2met2_cn/_build/html/_sources/赛道设置与评估.md.txt
@@ -1,6 +1,6 @@
 # 赛道设置与评估
-## 说话人相关的语音识别 (主赛道)
-说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，组织者将不提供耳机的近场音频、转录以及真实时间戳。主办方将不再提供每个说话人的真实时间戳，而是在Test-2023集上提供包含多个说话人的片段。这些片段可以通过一个简单的vad模型获得。
+## 说话人相关的语音识别
+说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。
 
 ![task difference](images/task_diff.png)
 
@@ -12,6 +12,6 @@ $$ \text{CER} = \frac {\mathcal N_{\text{Ins}} + \mathcal N_{\text{Sub}} + \math
 其中 $\mathcal N_{\text{Ins}}$ , $\mathcal N_{\text{Sub}}$ , $\mathcal N_{\text{Del}}$ 是三种错误的字符数, $\mathcal N_{\text{Total}}$ 是字符总数.
 ## 子赛道设置
 ### 子赛道一 (限定训练数据):
-参赛者在系统构建过程中仅能使用AliMeeting、AISHELL-4和CN Celeb，严禁使用额外数据。参赛者可以任何第三方开源的预训练模型，如[Hugging Face](https://huggingface.co/models)以及[ModelScope](https://www.modelscope.cn/models)上提供的模型。参赛者需要在最终的系统描述文档中详细列出使用的预训练模型名称以及链接。
+参赛者在系统构建过程中仅能使用AliMeeting、AISHELL-4和CN-Celeb，严禁使用额外数据。参赛者可以任何第三方开源的预训练模型，如[Hugging Face](https://huggingface.co/models)以及[ModelScope](https://www.modelscope.cn/models)上提供的模型。参赛者需要在最终的系统描述文档中详细列出使用的预训练模型名称以及链接。
 ### 子赛道二 (开放训练数据):
 除了限定数据外，参与者可以使用任何公开可用、私人录制和模拟仿真的数据集。但是，参与者必须清楚地列出使用的数据。同样，参赛者也可以使用任何第三方开源的预训练模型，但必须在最后的系统描述文件中明确的列出所使用的数据和模型链接，如果使用模拟仿真数据，请详细描述数据模拟的方案。
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diff --git a/docs_m2met2_cn/_build/html/genindex.html b/docs_m2met2_cn/_build/html/genindex.html
index 98b17f018..5558bcf2f 100644
--- a/docs_m2met2_cn/_build/html/genindex.html
+++ b/docs_m2met2_cn/_build/html/genindex.html
@@ -80,7 +80,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">赛道设置与评估</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
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diff --git a/docs_m2met2_cn/_build/html/index.html b/docs_m2met2_cn/_build/html/index.html
index 5e92ef3d7..fbc2fceac 100644
--- a/docs_m2met2_cn/_build/html/index.html
+++ b/docs_m2met2_cn/_build/html/index.html
@@ -85,7 +85,7 @@
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-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">子赛道设置</a></li>
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diff --git a/docs_m2met2_cn/_build/html/search.html b/docs_m2met2_cn/_build/html/search.html
index 0257e4c2e..4fe0684db 100644
--- a/docs_m2met2_cn/_build/html/search.html
+++ b/docs_m2met2_cn/_build/html/search.html
@@ -73,7 +73,7 @@
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-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">子赛道设置</a></li>
 </ul>
diff --git a/docs_m2met2_cn/_build/html/searchindex.js b/docs_m2met2_cn/_build/html/searchindex.js
index 5a84a48fe..e9ff2b1c1 100644
--- a/docs_m2met2_cn/_build/html/searchindex.js
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diff --git a/docs_m2met2_cn/_build/html/基线.html b/docs_m2met2_cn/_build/html/基线.html
index 322a26197..f28043ea7 100644
--- a/docs_m2met2_cn/_build/html/基线.html
+++ b/docs_m2met2_cn/_build/html/基线.html
@@ -89,7 +89,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">赛道设置与评估</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">子赛道设置</a></li>
 </ul>
diff --git a/docs_m2met2_cn/_build/html/数据集.html b/docs_m2met2_cn/_build/html/数据集.html
index 276f7b76e..ddefcc188 100644
--- a/docs_m2met2_cn/_build/html/数据集.html
+++ b/docs_m2met2_cn/_build/html/数据集.html
@@ -89,7 +89,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">赛道设置与评估</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">子赛道设置</a></li>
 </ul>
@@ -131,10 +131,10 @@
 </section>
 <section id="alimeeting">
 <h2>Alimeeting数据集介绍<a class="headerlink" href="#alimeeting" title="此标题的永久链接">¶</a></h2>
-<p>AliMeeting总共包含118.75小时的语音数据，包括104.75小时的训练集（Train）、4小时的验证集（Eval）和10小时的测试集（Test）。Train集和Eval集分别包含212场和8场会议，其中每场会议由多个说话人进行15到30分钟的讨论。Train和Eval集中参与会议的总人数分别为456人和25人，并且参会的男女比例人数均衡。</p>
+<p>AliMeeting总共包含118.75小时的语音数据，包括104.75小时的训练集（Train）、4小时的验证集（Eval）和10小时的测试集（Test）。Train集，Eval集和Test集分别包含212场和8场会议，其中每场会议由多个说话人进行15到30分钟的讨论。Train，Eval和Test集中参与会议的总人数分别为456人和25人，并且参会的男女比例人数均衡。</p>
 <p>该数据集收集于13个不同的会议室，按照大小规格分为小型、中型和大型三种，房间面积从8到55平方米不等。不同房间具有不同的布局和声学特性，每个房间的详细参数也将发送给参与者。会议场地的墙体材料类型包括水泥、玻璃等。会议场地的家具包括沙发、电视、黑板、风扇、空调、植物等。在录制过程中，麦克风阵列放置于桌上，多个说话人围坐在桌边进行自然对话。麦克风阵列离说话人距离约0.3到5.0米之间。所有说话人的母语均是汉语，并且说的都是普通话，没有浓重的口音。在会议录制期间可能会产生各种室内的噪音，包括键盘声、开门/关门声、风扇声、气泡声等。所有说话人在会议的录制期间均保持相同位置，不发生走动。训练集和验证集的说话人没有重复。图1展示了一个会议室的布局以及麦克风的拓扑结构。</p>
 <p><img alt="meeting room" src="_images/meeting_room.png" /></p>
-<p>每场会议的说话人数量从2到4人不等。同时为了覆盖各种内容的会议场景，我们选择了多种会议主题，包括医疗、教育、商业、组织管理、工业生产等不同内容的例会。Train集，Eval集和Test集的平均语音重叠率分别为42.27%和34.76%。AliMeeting Train集，Eval集和Test集的详细信息见表1。表2显示了Train集,Eval集和Test集中不同发言者人数会议的语音重叠率和会议数量。</p>
+<p>每场会议的说话人数量从2到4人不等。同时为了覆盖各种内容的会议场景，我们选择了多种会议主题，包括医疗、教育、商业、组织管理、工业生产等不同内容的例会。Train集，Eval集和Test集的平均语音重叠率分别为42.27%，34.76%和42.8%。AliMeeting Train集，Eval集和Test集的详细信息见表1。表2显示了Train集,Eval集和Test集中不同发言者人数会议的语音重叠率和会议数量。</p>
 <p><img alt="dataset detail" src="_images/dataset_detail.png" />
 Test-2023测试集由20场会议组成，这些会议是在与AliMeeting数据集相同的声学环境下录制的。Test-2023测试集中的每个会议环节由2到4个参与者组成并且与AliMeeting测试集的配置相似。</p>
 <p>我们还使用耳机麦克风记录了每个说话人的近场音频信号，并确保只转录对应说话人自己的语音。需要注意的是，麦克风阵列记录的远场音频和耳机麦克风记录的近场音频在时间上是同步的。每场会议的所有抄本均以TextGrid格式存储，内容包括会议的时长、说话人信息（说话人数量、说话人ID、性别等）、每个说话人的片段总数、每个片段的时间戳和转录内容。</p>
diff --git a/docs_m2met2_cn/_build/html/简介.html b/docs_m2met2_cn/_build/html/简介.html
index a75969a41..917a2ec31 100644
--- a/docs_m2met2_cn/_build/html/简介.html
+++ b/docs_m2met2_cn/_build/html/简介.html
@@ -90,7 +90,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">赛道设置与评估</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">子赛道设置</a></li>
 </ul>
@@ -150,6 +150,7 @@
 <section id="id3">
 <h2>竞赛报名<a class="headerlink" href="#id3" title="此标题的永久链接">¶</a></h2>
 <p>来自学术界和工业界的有意向参赛者均应在2023年5月5日及之前填写下方的谷歌表单：</p>
+<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MET2.0报名</a></p>
 <p>主办方将在3个工作日内通过电子邮件通知符合条件的参赛团队，团队必须遵守将在挑战网站上发布的挑战规则。在排名发布之前，每个参赛者必须提交一份系统描述文件，详细说明使用的方法和模型。主办方将选择前三名纳入ASRU2023论文集。</p>
 </section>
 </section>
diff --git a/docs_m2met2_cn/_build/html/组委会.html b/docs_m2met2_cn/_build/html/组委会.html
index 3fac9f690..ddf93bb58 100644
--- a/docs_m2met2_cn/_build/html/组委会.html
+++ b/docs_m2met2_cn/_build/html/组委会.html
@@ -89,7 +89,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">赛道设置与评估</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">子赛道设置</a></li>
 </ul>
diff --git a/docs_m2met2_cn/_build/html/联系方式.html b/docs_m2met2_cn/_build/html/联系方式.html
index 4486e13fe..249e5dd69 100644
--- a/docs_m2met2_cn/_build/html/联系方式.html
+++ b/docs_m2met2_cn/_build/html/联系方式.html
@@ -85,7 +85,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">赛道设置与评估</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">子赛道设置</a></li>
 </ul>
diff --git a/docs_m2met2_cn/_build/html/规则.html b/docs_m2met2_cn/_build/html/规则.html
index 6a66fd730..dafef6684 100644
--- a/docs_m2met2_cn/_build/html/规则.html
+++ b/docs_m2met2_cn/_build/html/规则.html
@@ -89,7 +89,7 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html">赛道设置与评估</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="%E8%B5%9B%E9%81%93%E8%AE%BE%E7%BD%AE%E4%B8%8E%E8%AF%84%E4%BC%B0.html#id4">子赛道设置</a></li>
 </ul>
diff --git a/docs_m2met2_cn/_build/html/赛道设置与评估.html b/docs_m2met2_cn/_build/html/赛道设置与评估.html
index b9876b438..072ea5467 100644
--- a/docs_m2met2_cn/_build/html/赛道设置与评估.html
+++ b/docs_m2met2_cn/_build/html/赛道设置与评估.html
@@ -90,7 +90,7 @@
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">赛道设置与评估</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#id2">说话人相关的语音识别 (主赛道)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#id2">说话人相关的语音识别</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#id3">评估方法</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#id4">子赛道设置</a></li>
 </ul>
@@ -127,8 +127,8 @@
   <section id="id1">
 <h1>赛道设置与评估<a class="headerlink" href="#id1" title="此标题的永久链接">¶</a></h1>
 <section id="id2">
-<h2>说话人相关的语音识别 (主赛道)<a class="headerlink" href="#id2" title="此标题的永久链接">¶</a></h2>
-<p>说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，组织者将不提供耳机的近场音频、转录以及真实时间戳。主办方将不再提供每个说话人的真实时间戳，而是在Test-2023集上提供包含多个说话人的片段。这些片段可以通过一个简单的vad模型获得。</p>
+<h2>说话人相关的语音识别<a class="headerlink" href="#id2" title="此标题的永久链接">¶</a></h2>
+<p>说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。</p>
 <p><img alt="task difference" src="_images/task_diff.png" /></p>
 </section>
 <section id="id3">
@@ -142,7 +142,7 @@
 <h2>子赛道设置<a class="headerlink" href="#id4" title="此标题的永久链接">¶</a></h2>
 <section id="id5">
 <h3>子赛道一 (限定训练数据):<a class="headerlink" href="#id5" title="此标题的永久链接">¶</a></h3>
-<p>参赛者在系统构建过程中仅能使用AliMeeting、AISHELL-4和CN Celeb，严禁使用额外数据。参赛者可以任何第三方开源的预训练模型，如<a class="reference external" href="https://huggingface.co/models">Hugging Face</a>以及<a class="reference external" href="https://www.modelscope.cn/models">ModelScope</a>上提供的模型。参赛者需要在最终的系统描述文档中详细列出使用的预训练模型名称以及链接。</p>
+<p>参赛者在系统构建过程中仅能使用AliMeeting、AISHELL-4和CN-Celeb，严禁使用额外数据。参赛者可以任何第三方开源的预训练模型，如<a class="reference external" href="https://huggingface.co/models">Hugging Face</a>以及<a class="reference external" href="https://www.modelscope.cn/models">ModelScope</a>上提供的模型。参赛者需要在最终的系统描述文档中详细列出使用的预训练模型名称以及链接。</p>
 </section>
 <section id="id6">
 <h3>子赛道二 (开放训练数据):<a class="headerlink" href="#id6" title="此标题的永久链接">¶</a></h3>
diff --git a/docs_m2met2_cn/数据集.md b/docs_m2met2_cn/数据集.md
index 52965a1c5..24bfaf3d9 100644
--- a/docs_m2met2_cn/数据集.md
+++ b/docs_m2met2_cn/数据集.md
@@ -3,13 +3,13 @@
 在限定数据集条件下，训练数据集仅限于三个公开的语料库，即AliMeeting、AISHELL-4和CN-Celeb。为了评估参赛者提交的模型的性能，我们将发布一个新的测试集（Test-2023）用于打分和排名。下面我们将详细描述AliMeeting数据集和Test-2023测试集。
 
 ## Alimeeting数据集介绍
-AliMeeting总共包含118.75小时的语音数据，包括104.75小时的训练集（Train）、4小时的验证集（Eval）和10小时的测试集（Test）。Train集和Eval集分别包含212场和8场会议，其中每场会议由多个说话人进行15到30分钟的讨论。Train和Eval集中参与会议的总人数分别为456人和25人，并且参会的男女比例人数均衡。
+AliMeeting总共包含118.75小时的语音数据，包括104.75小时的训练集（Train）、4小时的验证集（Eval）和10小时的测试集（Test）。Train集，Eval集和Test集分别包含212场和8场会议，其中每场会议由多个说话人进行15到30分钟的讨论。Train，Eval和Test集中参与会议的总人数分别为456人和25人，并且参会的男女比例人数均衡。
 
 该数据集收集于13个不同的会议室，按照大小规格分为小型、中型和大型三种，房间面积从8到55平方米不等。不同房间具有不同的布局和声学特性，每个房间的详细参数也将发送给参与者。会议场地的墙体材料类型包括水泥、玻璃等。会议场地的家具包括沙发、电视、黑板、风扇、空调、植物等。在录制过程中，麦克风阵列放置于桌上，多个说话人围坐在桌边进行自然对话。麦克风阵列离说话人距离约0.3到5.0米之间。所有说话人的母语均是汉语，并且说的都是普通话，没有浓重的口音。在会议录制期间可能会产生各种室内的噪音，包括键盘声、开门/关门声、风扇声、气泡声等。所有说话人在会议的录制期间均保持相同位置，不发生走动。训练集和验证集的说话人没有重复。图1展示了一个会议室的布局以及麦克风的拓扑结构。
 
 ![meeting room](images/meeting_room.png)
 
-每场会议的说话人数量从2到4人不等。同时为了覆盖各种内容的会议场景，我们选择了多种会议主题，包括医疗、教育、商业、组织管理、工业生产等不同内容的例会。Train集，Eval集和Test集的平均语音重叠率分别为42.27\%和34.76\%。AliMeeting Train集，Eval集和Test集的详细信息见表1。表2显示了Train集,Eval集和Test集中不同发言者人数会议的语音重叠率和会议数量。
+每场会议的说话人数量从2到4人不等。同时为了覆盖各种内容的会议场景，我们选择了多种会议主题，包括医疗、教育、商业、组织管理、工业生产等不同内容的例会。Train集，Eval集和Test集的平均语音重叠率分别为42.27\%，34.76\%和42.8\%。AliMeeting Train集，Eval集和Test集的详细信息见表1。表2显示了Train集,Eval集和Test集中不同发言者人数会议的语音重叠率和会议数量。
 
 ![dataset detail](images/dataset_detail.png)
 Test-2023测试集由20场会议组成，这些会议是在与AliMeeting数据集相同的声学环境下录制的。Test-2023测试集中的每个会议环节由2到4个参与者组成并且与AliMeeting测试集的配置相似。
diff --git a/docs_m2met2_cn/简介.md b/docs_m2met2_cn/简介.md
index cf4dfb86a..4ae9b6759 100644
--- a/docs_m2met2_cn/简介.md
+++ b/docs_m2met2_cn/简介.md
@@ -25,4 +25,6 @@ ASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说话
 
 来自学术界和工业界的有意向参赛者均应在2023年5月5日及之前填写下方的谷歌表单：
 
+[M2MET2.0报名](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
+
 主办方将在3个工作日内通过电子邮件通知符合条件的参赛团队，团队必须遵守将在挑战网站上发布的挑战规则。在排名发布之前，每个参赛者必须提交一份系统描述文件，详细说明使用的方法和模型。主办方将选择前三名纳入ASRU2023论文集。
\ No newline at end of file
diff --git a/docs_m2met2_cn/赛道设置与评估.md b/docs_m2met2_cn/赛道设置与评估.md
index a99c1eddc..94a623690 100644
--- a/docs_m2met2_cn/赛道设置与评估.md
+++ b/docs_m2met2_cn/赛道设置与评估.md
@@ -1,6 +1,6 @@
 # 赛道设置与评估
-## 说话人相关的语音识别 (主赛道)
-说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，组织者将不提供耳机的近场音频、转录以及真实时间戳。主办方将不再提供每个说话人的真实时间戳，而是在Test-2023集上提供包含多个说话人的片段。这些片段可以通过一个简单的vad模型获得。
+## 说话人相关的语音识别
+说话人相关的ASR任务需要从重叠的语音中识别每个说话人的语音，并为识别内容分配一个说话人标签。图2展示了说话人相关语音识别任务和多说话人语音识别任务的主要区别。在本次竞赛中AliMeeting、Aishell4和Cn-Celeb数据集可作为受限数据源。在M2MeT挑战赛中使用的AliMeeting数据集包含训练、评估和测试集，在M2MET2.0可以在训练和评估中使用。此外，一个包含约10小时会议数据的新的Test-2023集将根据赛程安排发布并用于挑战赛的评分和排名。值得注意的是，对于Test-2023测试集，主办方将不再提供耳机的近场音频、转录以及真实时间戳。而是提供可以通过一个简单的VAD模型得到的包含多个说话人的片段。
 
 ![task difference](images/task_diff.png)
 
@@ -12,6 +12,6 @@ $$ \text{CER} = \frac {\mathcal N_{\text{Ins}} + \mathcal N_{\text{Sub}} + \math
 其中 $\mathcal N_{\text{Ins}}$ , $\mathcal N_{\text{Sub}}$ , $\mathcal N_{\text{Del}}$ 是三种错误的字符数, $\mathcal N_{\text{Total}}$ 是字符总数.
 ## 子赛道设置
 ### 子赛道一 (限定训练数据):
-参赛者在系统构建过程中仅能使用AliMeeting、AISHELL-4和CN Celeb，严禁使用额外数据。参赛者可以任何第三方开源的预训练模型，如[Hugging Face](https://huggingface.co/models)以及[ModelScope](https://www.modelscope.cn/models)上提供的模型。参赛者需要在最终的系统描述文档中详细列出使用的预训练模型名称以及链接。
+参赛者在系统构建过程中仅能使用AliMeeting、AISHELL-4和CN-Celeb，严禁使用额外数据。参赛者可以任何第三方开源的预训练模型，如[Hugging Face](https://huggingface.co/models)以及[ModelScope](https://www.modelscope.cn/models)上提供的模型。参赛者需要在最终的系统描述文档中详细列出使用的预训练模型名称以及链接。
 ### 子赛道二 (开放训练数据):
 除了限定数据外，参与者可以使用任何公开可用、私人录制和模拟仿真的数据集。但是，参与者必须清楚地列出使用的数据。同样，参赛者也可以使用任何第三方开源的预训练模型，但必须在最后的系统描述文件中明确的列出所使用的数据和模型链接，如果使用模拟仿真数据，请详细描述数据模拟的方案。
\ No newline at end of file

From 21114c2704db1a6d9633bd012a4320198eea62bd Mon Sep 17 00:00:00 2001
From: yhliang <429259365@qq.com>
Date: Fri, 21 Apr 2023 11:49:09 +0800
Subject: [PATCH 3/4] update m2met2 docs

---
 docs_m2met2/Introduction.md                   |  19 ++++++++--------
 docs_m2met2/Rules.md                          |   2 --
 .../_build/doctrees/Introduction.doctree      | Bin 15176 -> 15878 bytes
 docs_m2met2/_build/doctrees/Rules.doctree     | Bin 6927 -> 6546 bytes
 .../_build/doctrees/environment.pickle        | Bin 25894 -> 25894 bytes
 docs_m2met2/_build/html/Introduction.html     |  20 +++++++++--------
 docs_m2met2/_build/html/Rules.html            |   1 -
 .../_build/html/_sources/Introduction.md.txt  |  21 +++++++++---------
 docs_m2met2/_build/html/_sources/Rules.md.txt |   2 --
 docs_m2met2/_build/html/searchindex.js        |   2 +-
 .../_build/doctrees/environment.pickle        | Bin 25266 -> 25266 bytes
 docs_m2met2_cn/_build/doctrees/简介.doctree   | Bin 12888 -> 13572 bytes
 docs_m2met2_cn/_build/doctrees/规则.doctree   | Bin 6073 -> 5722 bytes
 .../_build/html/_sources/简介.md.txt          |   8 ++++---
 .../_build/html/_sources/规则.md.txt          |   2 --
 docs_m2met2_cn/_build/html/searchindex.js     |   2 +-
 docs_m2met2_cn/_build/html/简介.html          |   8 ++++---
 docs_m2met2_cn/_build/html/规则.html          |   1 -
 docs_m2met2_cn/简介.md                        |   8 ++++---
 docs_m2met2_cn/规则.md                        |   2 --
 20 files changed, 49 insertions(+), 49 deletions(-)

diff --git a/docs_m2met2/Introduction.md b/docs_m2met2/Introduction.md
index b7f783ebb..eac9eb6b5 100644
--- a/docs_m2met2/Introduction.md
+++ b/docs_m2met2/Introduction.md
@@ -9,18 +9,19 @@ The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises tw
 Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying "who spoke what at when". To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models' performance and advance the state of the art in this area.
 
 ## Timeline(AOE Time)
-
-- $ May~5^{th}, 2023: $ Registration deadline, the due date for participants to join the Challenge.
-- $ June~9^{th}, 2023: $ Test data release.
-- $ June~13^{rd}, 2023: $ Final submission deadline.
-- $ June~19^{th}, 2023: $ Evaluation result and ranking release.
-- $ July~3^{rd}, 2023: $ Deadline for paper submission.
-- $ July~10^{th}, 2023: $ Deadline for final paper submission.
-- $ December~12^{nd}\ to\ 16^{th}, 2023: $ ASRU Workshop
+- $ April~29, 2023: $ Challenge and registration open.
+- $ May~8, 2023: $ Baseline release.
+- $ May~15, 2023: $ Registration deadline, the due date for participants to join the Challenge.
+- $ June~9, 2023: $ Test data release and leaderboard open.
+- $ June~13, 2023: $ Final submission deadline.
+- $ June~19, 2023: $ Evaluation result and ranking release.
+- $ July~3, 2023: $ Deadline for paper submission.
+- $ July~10, 2023: $ Deadline for final paper submission.
+- $ December~12\ to\ 16, 2023: $ ASRU Workshop and challenge session
 
 ## Guidelines
 
-Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 5, 2023. 
+Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 15, 2023. 
 
 [M2MET2.0 Registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
 
diff --git a/docs_m2met2/Rules.md b/docs_m2met2/Rules.md
index 8bc1a1900..340c2f50d 100644
--- a/docs_m2met2/Rules.md
+++ b/docs_m2met2/Rules.md
@@ -4,8 +4,6 @@ All participants should adhere to the following rules to be eligible for the cha
 - Data augmentation is allowed on the original training dataset, including, but not limited to, adding noise or reverberation, speed perturbation and tone change.
 
 - Participants are permitted to use the Eval set for model training, but it is not allowed to use the Test set for this purpose. Instead, the Test set should only be utilized for parameter tuning and model selection. Any use of the Test-2023 dataset that violates these rules is strictly prohibited, including but not limited to the use of the Test set for fine-tuning or training the model.
-
-- Multi-system fusion is allowed, but the systems with same structure and different parameters is not encouraged.
   
 - If the cpCER of the two systems on the Test dataset are the same, the system with lower computation complexity will be judged as the superior one.
   
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diff --git a/docs_m2met2/_build/html/Introduction.html b/docs_m2met2/_build/html/Introduction.html
index a1950685f..2ddafe22c 100644
--- a/docs_m2met2/_build/html/Introduction.html
+++ b/docs_m2met2/_build/html/Introduction.html
@@ -135,19 +135,21 @@
 <section id="timeline-aoe-time">
 <h2>Timeline(AOE Time)<a class="headerlink" href="#timeline-aoe-time" title="Permalink to this heading">¶</a></h2>
 <ul class="simple">
-<li><p><span class="math notranslate nohighlight">\( May~5^{th}, 2023: \)</span> Registration deadline, the due date for participants to join the Challenge.</p></li>
-<li><p><span class="math notranslate nohighlight">\( June~9^{th}, 2023: \)</span> Test data release.</p></li>
-<li><p><span class="math notranslate nohighlight">\( June~13^{rd}, 2023: \)</span> Final submission deadline.</p></li>
-<li><p><span class="math notranslate nohighlight">\( June~19^{th}, 2023: \)</span> Evaluation result and ranking release.</p></li>
-<li><p><span class="math notranslate nohighlight">\( July~3^{rd}, 2023: \)</span> Deadline for paper submission.</p></li>
-<li><p><span class="math notranslate nohighlight">\( July~10^{th}, 2023: \)</span> Deadline for final paper submission.</p></li>
-<li><p><span class="math notranslate nohighlight">\( December~12^{nd}\ to\ 16^{th}, 2023: \)</span> ASRU Workshop</p></li>
+<li><p><span class="math notranslate nohighlight">\( April~29, 2023: \)</span> Challenge and registration open.</p></li>
+<li><p><span class="math notranslate nohighlight">\( May~8, 2023: \)</span> Baseline release.</p></li>
+<li><p><span class="math notranslate nohighlight">\( May~15, 2023: \)</span> Registration deadline, the due date for participants to join the Challenge.</p></li>
+<li><p><span class="math notranslate nohighlight">\( June~9, 2023: \)</span> Test data release and leaderboard open.</p></li>
+<li><p><span class="math notranslate nohighlight">\( June~13, 2023: \)</span> Final submission deadline.</p></li>
+<li><p><span class="math notranslate nohighlight">\( June~19, 2023: \)</span> Evaluation result and ranking release.</p></li>
+<li><p><span class="math notranslate nohighlight">\( July~3, 2023: \)</span> Deadline for paper submission.</p></li>
+<li><p><span class="math notranslate nohighlight">\( July~10, 2023: \)</span> Deadline for final paper submission.</p></li>
+<li><p><span class="math notranslate nohighlight">\( December~12\ to\ 16, 2023: \)</span> ASRU Workshop and challenge session</p></li>
 </ul>
 </section>
 <section id="guidelines">
 <h2>Guidelines<a class="headerlink" href="#guidelines" title="Permalink to this heading">¶</a></h2>
-<p>Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 5, 2023.</p>
-<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MET2.0 registration</a></p>
+<p>Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 15, 2023.</p>
+<p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MET2.0 Registration</a></p>
 <p>Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top three submissions to be included in the ASRU2023 Proceedings.</p>
 </section>
 </section>
diff --git a/docs_m2met2/_build/html/Rules.html b/docs_m2met2/_build/html/Rules.html
index 8eef8aad7..59651158a 100644
--- a/docs_m2met2/_build/html/Rules.html
+++ b/docs_m2met2/_build/html/Rules.html
@@ -128,7 +128,6 @@
 <ul class="simple">
 <li><p>Data augmentation is allowed on the original training dataset, including, but not limited to, adding noise or reverberation, speed perturbation and tone change.</p></li>
 <li><p>Participants are permitted to use the Eval set for model training, but it is not allowed to use the Test set for this purpose. Instead, the Test set should only be utilized for parameter tuning and model selection. Any use of the Test-2023 dataset that violates these rules is strictly prohibited, including but not limited to the use of the Test set for fine-tuning or training the model.</p></li>
-<li><p>Multi-system fusion is allowed, but the systems with same structure and different parameters is not encouraged.</p></li>
 <li><p>If the cpCER of the two systems on the Test dataset are the same, the system with lower computation complexity will be judged as the superior one.</p></li>
 <li><p>If the forced alignment is used to obtain the frame-level classification label, the forced alignment model must be trained on the basis of the data allowed by the corresponding sub-track.</p></li>
 <li><p>Shallow fusion is allowed to the end-to-end approaches, e.g., LAS, RNNT and Transformer, but the training data of the shallow fusion language model can only come from the transcripts of the allowed training dataset.</p></li>
diff --git a/docs_m2met2/_build/html/_sources/Introduction.md.txt b/docs_m2met2/_build/html/_sources/Introduction.md.txt
index 27322dece..eac9eb6b5 100644
--- a/docs_m2met2/_build/html/_sources/Introduction.md.txt
+++ b/docs_m2met2/_build/html/_sources/Introduction.md.txt
@@ -9,19 +9,20 @@ The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises tw
 Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying "who spoke what at when". To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models' performance and advance the state of the art in this area.
 
 ## Timeline(AOE Time)
-
-- $ May~5^{th}, 2023: $ Registration deadline, the due date for participants to join the Challenge.
-- $ June~9^{th}, 2023: $ Test data release.
-- $ June~13^{rd}, 2023: $ Final submission deadline.
-- $ June~19^{th}, 2023: $ Evaluation result and ranking release.
-- $ July~3^{rd}, 2023: $ Deadline for paper submission.
-- $ July~10^{th}, 2023: $ Deadline for final paper submission.
-- $ December~12^{nd}\ to\ 16^{th}, 2023: $ ASRU Workshop
+- $ April~29, 2023: $ Challenge and registration open.
+- $ May~8, 2023: $ Baseline release.
+- $ May~15, 2023: $ Registration deadline, the due date for participants to join the Challenge.
+- $ June~9, 2023: $ Test data release and leaderboard open.
+- $ June~13, 2023: $ Final submission deadline.
+- $ June~19, 2023: $ Evaluation result and ranking release.
+- $ July~3, 2023: $ Deadline for paper submission.
+- $ July~10, 2023: $ Deadline for final paper submission.
+- $ December~12\ to\ 16, 2023: $ ASRU Workshop and challenge session
 
 ## Guidelines
 
-Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 5, 2023. 
+Interested participants, whether from academia or industry, must register for the challenge by completing the Google form below. The deadline for registration is May 15, 2023. 
 
-[M2MET2.0 registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
+[M2MET2.0 Registration](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
 
 Within three working days, the challenge organizer will send email invitations to eligible teams to participate in the challenge. All qualified teams are required to adhere to the challenge rules, which will be published on the challenge page. Prior to the ranking release time, each participant must submit a system description document detailing their approach and methods. The organizer will select the top three submissions to be included in the ASRU2023 Proceedings. 
diff --git a/docs_m2met2/_build/html/_sources/Rules.md.txt b/docs_m2met2/_build/html/_sources/Rules.md.txt
index 8bc1a1900..340c2f50d 100644
--- a/docs_m2met2/_build/html/_sources/Rules.md.txt
+++ b/docs_m2met2/_build/html/_sources/Rules.md.txt
@@ -4,8 +4,6 @@ All participants should adhere to the following rules to be eligible for the cha
 - Data augmentation is allowed on the original training dataset, including, but not limited to, adding noise or reverberation, speed perturbation and tone change.
 
 - Participants are permitted to use the Eval set for model training, but it is not allowed to use the Test set for this purpose. Instead, the Test set should only be utilized for parameter tuning and model selection. Any use of the Test-2023 dataset that violates these rules is strictly prohibited, including but not limited to the use of the Test set for fine-tuning or training the model.
-
-- Multi-system fusion is allowed, but the systems with same structure and different parameters is not encouraged.
   
 - If the cpCER of the two systems on the Test dataset are the same, the system with lower computation complexity will be judged as the superior one.
   
diff --git a/docs_m2met2/_build/html/searchindex.js b/docs_m2met2/_build/html/searchindex.js
index 6fd42560b..54443a001 100644
--- a/docs_m2met2/_build/html/searchindex.js
+++ b/docs_m2met2/_build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["Baseline", "Contact", "Dataset", "Introduction", "Organizers", "Rules", "Track_setting_and_evaluation", "index"], "filenames": ["Baseline.md", "Contact.md", "Dataset.md", "Introduction.md", "Organizers.md", "Rules.md", "Track_setting_and_evaluation.md", "index.rst"], "titles": ["Baseline", "Contact", "Datasets", "Introduction", "Organizers", "Rules", "Track &amp; Evaluation", "ASRU 2023 MULTI-CHANNEL MULTI-PARTY MEETING TRANSCRIPTION CHALLENGE 2.0 (M2MeT2.0)"], "terms": {"we": [0, 2, 3, 7], "releas": [0, 2, 3, 6], "an": [0, 2, 3, 6], "e2": 0, "sa": 0, "asr": [0, 3, 7], "cite": 0, "kanda21b_interspeech": 0, "conduct": [0, 2], "funasr": 0, "time": [0, 6], "accord": [0, 3], "timelin": [0, 2], "The": [0, 2, 3, 5, 6], "model": [0, 2, 3, 5, 6], "architectur": 0, "i": [0, 2, 3, 5], "shown": [0, 2], "figur": [0, 6], "3": [0, 2, 3], "speakerencod": 0, "initi": 0, "pre": [0, 6], "train": [0, 3, 5, 7], "speaker": [0, 2, 3, 7], "verif": 0, "from": [0, 2, 3, 5, 6], 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\ No newline at end of file
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diff --git a/docs_m2met2_cn/_build/html/_sources/简介.md.txt b/docs_m2met2_cn/_build/html/_sources/简介.md.txt
index 4ae9b6759..52df97d6b 100644
--- a/docs_m2met2_cn/_build/html/_sources/简介.md.txt
+++ b/docs_m2met2_cn/_build/html/_sources/简介.md.txt
@@ -13,17 +13,19 @@ ASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说话
 
 ## 时间安排(AOE时间)
 
-- $ 2023.5.5: $ 参赛者注册截止
+- $ 2023.4.29: $ 开放注册
+- $ 2023.5.8: $ 基线发布
+- $ 2023.5.15: $ 注册截止
 - $ 2023.6.9: $ 测试集数据发布
 - $ 2023.6.13: $ 最终结果提交截止
 - $ 2023.6.19: $ 评估结果和排名发布
 - $ 2023.7.3: $ 论文提交截止
 - $ 2023.7.10: $ 最终版论文提交截止
-- $ 2023.12.12: $ ASRU Workshop
+- $ 2023.12.12: $ ASRU Workshop & challenge session
 
 ## 竞赛报名
 
-来自学术界和工业界的有意向参赛者均应在2023年5月5日及之前填写下方的谷歌表单：
+来自学术界和工业界的有意向参赛者均应在2023年5月15日及之前填写下方的谷歌表单：
 
 [M2MET2.0报名](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
 
diff --git a/docs_m2met2_cn/_build/html/_sources/规则.md.txt b/docs_m2met2_cn/_build/html/_sources/规则.md.txt
index b0928ee54..68cb15972 100644
--- a/docs_m2met2_cn/_build/html/_sources/规则.md.txt
+++ b/docs_m2met2_cn/_build/html/_sources/规则.md.txt
@@ -4,8 +4,6 @@
 - 允许在原始训练数据集上进行数据增强，包括但不限于添加噪声或混响、速度扰动和音调变化。
 
 - 允许参赛者使用Eval集进行模型训练，但Test集仅能用于调参和模型选择，严禁以任何形式使用Test-2023数据集，包括但不限于使用测试数据集微调或训练模型。
-
-- 允许多系统融合，但不鼓励使用具有相同结构仅参数不同的子系统融合。
   
 - 如果两个系统的测试cpCER相同，则计算复杂度较低的系统将被认定为更优。
   
diff --git a/docs_m2met2_cn/_build/html/searchindex.js b/docs_m2met2_cn/_build/html/searchindex.js
index e9ff2b1c1..c9fe167cc 100644
--- a/docs_m2met2_cn/_build/html/searchindex.js
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diff --git a/docs_m2met2_cn/_build/html/简介.html b/docs_m2met2_cn/_build/html/简介.html
index 917a2ec31..f1da18e63 100644
--- a/docs_m2met2_cn/_build/html/简介.html
+++ b/docs_m2met2_cn/_build/html/简介.html
@@ -138,18 +138,20 @@
 <section id="aoe">
 <h2>时间安排(AOE时间)<a class="headerlink" href="#aoe" title="此标题的永久链接">¶</a></h2>
 <ul class="simple">
-<li><p><span class="math notranslate nohighlight">\( 2023.5.5: \)</span> 参赛者注册截止</p></li>
+<li><p><span class="math notranslate nohighlight">\( 2023.4.29: \)</span> 开放注册</p></li>
+<li><p><span class="math notranslate nohighlight">\( 2023.5.8: \)</span> 基线发布</p></li>
+<li><p><span class="math notranslate nohighlight">\( 2023.5.15: \)</span> 注册截止</p></li>
 <li><p><span class="math notranslate nohighlight">\( 2023.6.9: \)</span> 测试集数据发布</p></li>
 <li><p><span class="math notranslate nohighlight">\( 2023.6.13: \)</span> 最终结果提交截止</p></li>
 <li><p><span class="math notranslate nohighlight">\( 2023.6.19: \)</span> 评估结果和排名发布</p></li>
 <li><p><span class="math notranslate nohighlight">\( 2023.7.3: \)</span> 论文提交截止</p></li>
 <li><p><span class="math notranslate nohighlight">\( 2023.7.10: \)</span> 最终版论文提交截止</p></li>
-<li><p><span class="math notranslate nohighlight">\( 2023.12.12: \)</span> ASRU Workshop</p></li>
+<li><p><span class="math notranslate nohighlight">\( 2023.12.12: \)</span> ASRU Workshop &amp; challenge session</p></li>
 </ul>
 </section>
 <section id="id3">
 <h2>竞赛报名<a class="headerlink" href="#id3" title="此标题的永久链接">¶</a></h2>
-<p>来自学术界和工业界的有意向参赛者均应在2023年5月5日及之前填写下方的谷歌表单：</p>
+<p>来自学术界和工业界的有意向参赛者均应在2023年5月15日及之前填写下方的谷歌表单：</p>
 <p><a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link">M2MET2.0报名</a></p>
 <p>主办方将在3个工作日内通过电子邮件通知符合条件的参赛团队，团队必须遵守将在挑战网站上发布的挑战规则。在排名发布之前，每个参赛者必须提交一份系统描述文件，详细说明使用的方法和模型。主办方将选择前三名纳入ASRU2023论文集。</p>
 </section>
diff --git a/docs_m2met2_cn/_build/html/规则.html b/docs_m2met2_cn/_build/html/规则.html
index dafef6684..5186ebdf3 100644
--- a/docs_m2met2_cn/_build/html/规则.html
+++ b/docs_m2met2_cn/_build/html/规则.html
@@ -129,7 +129,6 @@
 <ul class="simple">
 <li><p>允许在原始训练数据集上进行数据增强，包括但不限于添加噪声或混响、速度扰动和音调变化。</p></li>
 <li><p>允许参赛者使用Eval集进行模型训练，但Test集仅能用于调参和模型选择，严禁以任何形式使用Test-2023数据集，包括但不限于使用测试数据集微调或训练模型。</p></li>
-<li><p>允许多系统融合，但不鼓励使用具有相同结构仅参数不同的子系统融合。</p></li>
 <li><p>如果两个系统的测试cpCER相同，则计算复杂度较低的系统将被认定为更优。</p></li>
 <li><p>如果使用强制对齐模型获得了逐帧分类标签，则必须使用相应子赛道允许的数据对强制对齐模型进行训练。</p></li>
 <li><p>端到端方法中允许使用浅层融合语言模型，模型可以选择LAS、RNNT和Transformer等，但浅层融合语言模型的训练数据只能来自于允许的训练数据集的转录抄本。</p></li>
diff --git a/docs_m2met2_cn/简介.md b/docs_m2met2_cn/简介.md
index 4ae9b6759..52df97d6b 100644
--- a/docs_m2met2_cn/简介.md
+++ b/docs_m2met2_cn/简介.md
@@ -13,17 +13,19 @@ ASSP2022 M2MeT挑战的侧重点是会议场景，它包括两个赛道：说话
 
 ## 时间安排(AOE时间)
 
-- $ 2023.5.5: $ 参赛者注册截止
+- $ 2023.4.29: $ 开放注册
+- $ 2023.5.8: $ 基线发布
+- $ 2023.5.15: $ 注册截止
 - $ 2023.6.9: $ 测试集数据发布
 - $ 2023.6.13: $ 最终结果提交截止
 - $ 2023.6.19: $ 评估结果和排名发布
 - $ 2023.7.3: $ 论文提交截止
 - $ 2023.7.10: $ 最终版论文提交截止
-- $ 2023.12.12: $ ASRU Workshop
+- $ 2023.12.12: $ ASRU Workshop & challenge session
 
 ## 竞赛报名
 
-来自学术界和工业界的有意向参赛者均应在2023年5月5日及之前填写下方的谷歌表单：
+来自学术界和工业界的有意向参赛者均应在2023年5月15日及之前填写下方的谷歌表单：
 
 [M2MET2.0报名](https://docs.google.com/forms/d/e/1FAIpQLSf77T9vAl7Ym-u5g8gXu18SBofoWRaFShBo26Ym0-HDxHW9PQ/viewform?usp=sf_link)
 
diff --git a/docs_m2met2_cn/规则.md b/docs_m2met2_cn/规则.md
index b0928ee54..68cb15972 100644
--- a/docs_m2met2_cn/规则.md
+++ b/docs_m2met2_cn/规则.md
@@ -4,8 +4,6 @@
 - 允许在原始训练数据集上进行数据增强，包括但不限于添加噪声或混响、速度扰动和音调变化。
 
 - 允许参赛者使用Eval集进行模型训练，但Test集仅能用于调参和模型选择，严禁以任何形式使用Test-2023数据集，包括但不限于使用测试数据集微调或训练模型。
-
-- 允许多系统融合，但不鼓励使用具有相同结构仅参数不同的子系统融合。
   
 - 如果两个系统的测试cpCER相同，则计算复杂度较低的系统将被认定为更优。
   

From 3ef7c5c966bc025088d65212aaafaed2be3ce982 Mon Sep 17 00:00:00 2001
From: yhliang <429259365@qq.com>
Date: Tue, 25 Apr 2023 17:23:12 +0800
Subject: [PATCH 4/4] update readme

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 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/_static/fonts/source-serif-pro/EOT/SourceSerifPro-Light.eot (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/_static/fonts/source-serif-pro/EOT/SourceSerifPro-Regular.eot (100%)
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 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/_static/fonts/source-serif-pro/OTF/SourceSerifPro-Bold.otf (100%)
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 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/_static/guzzle.css (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/_static/jquery-3.6.0.js (100%)
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 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/_static/searchtools.js (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/_static/sphinx_highlight.js (100%)
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 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/_static/underscore-1.13.1.js (100%)
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 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/objects.inv (100%)
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 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/基线.html (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/数据集.html (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/简介.html (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/组委会.html (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/联系方式.html (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/规则.html (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/_build/html/赛道设置与评估.html (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/conf.py (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/baseline_result.png (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/buhui.jpeg (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/chenzhuo.jpg (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/dataset_detail.png (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/dataset_details.png (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/kong.png (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/lxie.jpeg (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/meeting_room.png (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/qian.jpeg (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/sa_asr_arch.png (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/task_diff.png (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/wujian.jpg (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/zhijie.jpg (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/images/zsl.JPG (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/index.rst (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/make.bat (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/基线.md (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/数据集.md (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/简介.md (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/组委会.md (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/联系方式.md (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/规则.md (100%)
 rename {docs_m2met2_cn => docs/m2met2_cn}/赛道设置与评估.md (100%)

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 75651b690..26a47c478 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -29,10 +29,10 @@ jobs:
           cp -r docs/_build/html/* public/en/
           mkdir public/m2met2
           touch public/m2met2/.nojekyll
-          cp -r docs_m2met2/_build/html/* public/m2met2/
+          cp -r docs/m2met2/_build/html/* public/m2met2/
           mkdir public/m2met2_cn
           touch public/m2met2_cn/.nojekyll
-          cp -r docs_m2met2_cn/_build/html/* public/m2met2_cn/
+          cp -r docs/m2met2_cn/_build/html/* public/m2met2_cn/
 
       - name: deploy github.io pages
         if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/dev_wjm' || github.ref == 'refs/heads/dev_lyh'
diff --git a/README.md b/README.md
index 48b5ccdf8..21c1cc133 100644
--- a/README.md
+++ b/README.md
@@ -18,14 +18,13 @@
 | [**Runtime**](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime)
 | [**Model Zoo**](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/modelscope_models.md)
 | [**Contact**](#contact)
-|
-[**M2MET2.0 Guidence_CN**](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)
+| [**M2MET2.0 Guidence_CN**](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)
 | [**M2MET2.0 Guidence_EN**](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)
 
-## Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge
-We are pleased to announce that the M2MeT2.0 challenge will be held in the near future. The baseline system is conducted on FunASR and is provided as a receipe of AliMeeting corpus. For more details you can see the guidence of M2MET2.0 ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
 ## What's new: 
-
+### Multi-Channel Multi-Party Meeting Transcription 2.0 (M2MET2.0) Challenge
+We are pleased to announce that the M2MeT2.0 challenge will be held in the near future. The baseline system is conducted on FunASR and is provided as a receipe of AliMeeting corpus. For more details you can see the guidence of M2MET2.0 ([CN](https://alibaba-damo-academy.github.io/FunASR/m2met2_cn/index.html)/[EN](https://alibaba-damo-academy.github.io/FunASR/m2met2/index.html)).
+### Release notes
 For the release notes, please ref to [news](https://github.com/alibaba-damo-academy/FunASR/releases)
 
 ## Highlights
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