lwzzz7 / neuralspeech

NeuralSpeech

NeuralSpeech is a research project at Microsoft Research Asia, which focuses on neural network based speech processing, including automatic speech recognition (ASR), text-to-speech synthesis (TTS), spatial audio synthesis, video dubbing, etc.

Currently this repo covers several research work:

• Automatic Speech Recognition
  • FastCorrect, NeurIPS 2021
  • FastCorrect 2, EMNLP 2021 Findings
  • SoftCorrect, AAAI 2023
  • MaskCorrect, EMNLP 2022
  • CMatch for ASR, INTERSPEECH 2021
  • Adapter for ASR, TASLP 2022
• Text-to-Speech Synthesis
  • LightSpeech, ICASSP 2021
  • PriorGrad, ICLR 2022
  • PromptTTS 2, ICLR 2024
• Spatial Audio Synthesis
  • BinauralGrad, NeurIPS 2022
• Video Dubbing
  • VideoDubber, AAAI 2023

For more research in NeuralSpeech project, you can refer to this page: https://speechresearch.github.io/. We will release more research work in the future.

For our research on AI music, you can refer to our Muzic project: https://github.com/microsoft/muzic.

We are hiring!

We are hiring researchers on speech (speech synthesis, speech recognition, voice conversion, audio processing), natural language processing, and machine learning. Please contact Xu Tan ([email protected]) if you have interests.

Reference

If you find NeuralSpeech project useful in your work, you can cite the following papers:

• [1] FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition, Yichong Leng, Xu Tan, Linchen Zhu, Jin Xu, Renqian Luo, Linquan Liu, Tao Qin, Xiang-Yang Li, Ed Lin and Tie-Yan Liu, NeurIPS 2021.
• [2] FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition, Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Tao Qin, Xiang-Yang Li, Ed Lin, Tie-Yan Liu, Findings of EMNLP 2021.
• [3] SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition, Yichong Leng, Xu Tan, Wenjie Liu, Kaitao Song, Rui Wang, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu, AAAI 2023.
• [4] [MaskCorrect] Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction, Kai Shen, Yichong Leng, Xu Tan, Siliang Tang, Yuan Zhang, Wenjie Liu, Edward Lin, EMNLP 2022.
• [5] [CMatch] Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching, Wenxin Hou, Jindong Wang, Xu Tan, Tao Qin, Takahiro Shinozaki, INTERSPEECH 2021.
• [6] [Adapter] Exploiting Adapters for Cross-lingual Low-resource Speech Recognition, Wenxin Hou, Han Zhu, Yidong Wang, Jindong Wang, Tao Qin, Renjun Xu, Takahiro Shinozaki. IEEE/ACM TASLP 2022.
• [7] LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search, Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Jinzhu Li, Sheng Zhao, Enhong Chen and Tie-Yan Liu, ICASSP 2021.
• [8] PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior, Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu, ICLR 2022.
• [9] BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis, Yichong Leng, Zehua Chen, Junliang Guo, Haohe Liu, Jiawei Chen, Xu Tan, Danilo Mandic, Lei He, Xiang-Yang Li, Tao Qin, Sheng Zhao and Tie-Yan Liu, NeurIPS 2022.
• [10] VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing, Yihan Wu, Junliang Guo, Xu Tan, Chen Zhang, Bohan Li, Ruihua Song, Lei He, Sheng Zhao, Arul Menezes, Jiang Bian, AAAI 2022.
• [11] PromptTTS 2: Describing and Generating Voices with Text Prompt, Yichong Leng, Zhifang Guo, Kai Shen, Xu Tan, Zeqian Ju, Yanqing Liu, Yufei Liu, Dongchao Yang, Leying Zhang, Kaitao Song, Lei He, Xiang-Yang Li, Sheng Zhao, Tao Qin, Jiang Bian, ICLR 2024.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

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