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<section id="baseline">
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<h1>Baseline<a class="headerlink" href="#baseline" title="Permalink to this heading">¶</a></h1>
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<section id="overview">
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<h2>Overview<a class="headerlink" href="#overview" title="Permalink to this heading">¶</a></h2>
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<p>We will release an E2E SA-ASR~\cite{kanda21b_interspeech} baseline conducted on <a class="reference external" href="https://github.com/alibaba-damo-academy/FunASR">FunASR</a> at the time according to the timeline. The model architecture is shown in Figure 3. The SpeakerEncoder is initialized with a pre-trained speaker verification model from ModelScope. This speaker verification model is also be used to extract the speaker embedding in the speaker profile.</p>
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<p><img alt="model archietecture" src="_images/sa_asr_arch.png" /></p>
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</section>
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<section id="quick-start">
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<h2>Quick start<a class="headerlink" href="#quick-start" title="Permalink to this heading">¶</a></h2>
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<p>#TODO: fill with the README.md of the baseline</p>
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</section>
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<section id="baseline-results">
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<h2>Baseline results<a class="headerlink" href="#baseline-results" title="Permalink to this heading">¶</a></h2>
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<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.
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<img alt="baseline result" src="_images/baseline_result.png" /></p>
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