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# esp_sr [[中文]](./README_cn.md)
# esp_sr
Espressif esp_sr provides basic algorithms for **Speech Recognition** applications. Now, this framework has three modules:
@ -21,14 +21,13 @@ Espressif's speech command recognition model [MultiNet](speech_command_recogniti
Currently, Espressif **MultiNet** supports up to 100 Chinese or English speech commands, such as “打开空调” (Turn on the air conditioner) and “打开卧室灯” (Turn on the bedroom light).
## Acoustic algorithm
## Audio Front End
Espressif acoustic algorithm module is specially designed to improve speech recognition performance in far-field or noisy environments.
Espressif Audio Front-End [AFE](audio_front_end/README.md) integrates AEC (Acoustic Echo Cancellation), VAD (Voice Activity Detection),BSS (Blind Source Separation) and NS (Noise Suppression).
Currently, MASE algorithm supports 2-mic linear array and 3-mic circular array.
Our two-mic Audio Front-End (AFE) have been qualified as a “Software Audio Front-End Solution” for [Amazon Alexa Built-in devices](https://developer.amazon.com/en-US/alexa/solution-providers/dev-kits#software-audio-front-end-dev-kits).
**In order to achieve optimal performance:**
* Please refer to hardware design [ESP32_Korvo](https://github.com/espressif/esp-skainet/tree/master/docs/zh_CN/hw-reference/esp32/user-guide-esp32-korvo-v1.1.md) or [ESP32-LyraT-Mini](https://docs.espressif.com/projects/esp-adf/en/latest/get-started/get-started-esp32-lyrat-mini.html).
* Please refer to software design [esp-skainet](https://github.com/espressif/esp-skainet).

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# esp_sr [[English]](./README.md)
esp_sr 提供语音识别相关方向算法模型，目前主要包括三个模块：
* 唤醒词识别模型 [WakeNet](wake_word_engine/README_cn.md)
* 语音命令识别模型 [MultiNet](speech_command_recognition/README_cn.md)
* 声学算法：MASE(Mic Array Speech Enhancement), AEC(Acoustic Echo Cancellation), VAD(Voice Activity Detection), AGC(Automatic Gain Control), NS(Noise Suppression)
这些算法以组件的形式提供，因此可以轻松地将它们集成到您的项目中。
## 唤醒词识别
唤醒词模型 [WakeNet](wake_word_engine/README_cn.md)，致力于提供一个低资源消耗的的高性能模型，支持类似“Alexa”，“天猫精灵”，“小爱同学”等唤醒词的识别。
目前乐鑫免费开放“Hi，乐鑫”唤醒词。如果用户需要其它唤醒词，乐鑫提供有唤醒词定制服务，具体可参考 [乐鑫语音唤醒词定制流程](wake_word_engine/乐鑫语音唤醒词定制流程.md)。
## 语音命令识别
命令词识别模型 [MultiNet](speech_command_recognition/README_cn.md) ，致力于提供一个灵活的离线语音命词识别框架。用户可方便根据需求自定义语音命令，无需重新训练模型。
目前模型支持类似“打开空调”，“打开卧室灯”等中文命令词识别和"Turn on/off the light" 等英文命令词识别，自定义语音命令词最大个数为 100。
## 声学算法
声学算法模块, 致力于提高复杂声学环境下的语音识别性能。MASE算法可有效改善远程或嘈杂环境下的语音识别性能。
目前MASE算法支持2-mic线性阵列和3-mic环形阵列。
**算法性能与硬件设计与软件配置息息相关，为达到最优性能:**
* 硬件设计建议参考 [ESP32_Korvo](https://github.com/espressif/esp-skainet/tree/master/docs/zh_CN/hw-reference/esp32/user-guide-esp32-korvo-v1.1.md) 或 [ESP32-LyraT-Mini](https://docs.espressif.com/projects/esp-adf/en/latest/get-started/get-started-esp32-lyrat-mini.html)。
* 软件设计建议参考 [esp-skainet](https://github.com/espressif/esp-skainet) 中相关示例。