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【语音识别】从入门到精通——最全干货大合集！

 专知 2018-11-07 11:52
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语音识别
【导读】语音识别领域最全入门资料、论文、代码、产品大合集！包括语音识别，语音合成，声纹识别等内容，一文在手，带你走进语音识别的世界。

作者 | 刘斌

编辑 | Xiaowen



入门学习


语音识别研究的四大前沿方

https://blog.csdn.net/haima1998/article/details/79094341



深度学习入门论文（语音识别领域）

https://blog.csdn.net/youyuyixiu/article/details/53764218



论语音识别三大关键技术 

https://blog.csdn.net/qq_34231800/article/details/80189617



深度学习与语音识别—常用声学模型简介

https://blog.csdn.net/dujiajiyi_xue5211314/article/details/53943313



有趣的开源软件：语音识别工具Kaldi 

https://blog.csdn.net/AMDS123/article/details/70313780



神经网络-CNN结构和语音识别应用 

https://blog.csdn.net/xmdxcsj/article/details/54695995



语音识别概述 

https://blog.csdn.net/shichaog/article/details/72528637



端到端语音识别 

https://blog.csdn.net/xmdxcsj/article/details/70300546



Attention在语音识别中的应用 

https://blog.csdn.net/quheDiegooo/article/details/76842201



语音合成技术 

https://blog.csdn.net/wja8a45TJ1Xa/article/details/78599509?locationNum=8&fps=1



深度学习于语音合成研究综述 

https://blog.csdn.net/weixin_37598106/article/details/81513816



端到端的TTS深度学习模型tacotron(中文语音合成) 

https://blog.csdn.net/yunnangf/article/details/79585089



TACOTRON:端到端的语音合成 

https://blog.csdn.net/Left_Think/article/details/74905928



声纹识别技术简介  

https://www.cnblogs.com/wuxian11/p/6498699.html



声纹识别技术的现状、局限与趋势 

https://blog.csdn.net/jojozhangju/article/details/78637221



声纹识别 

https://www.jianshu.com/p/513dadeef1fd



Deep speaker介绍 

https://blog.csdn.net/Lauyeed/article/details/79936632



论文


语音识别 DNN 
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition(2012), George E. Dahl et al. 

https://ieeexplore.ieee.org/document/5740583/?part=1



Deep Neural Networks for Acoustic Modeling in Speech Recognition(2012), Geoffrey Hinton et al. 

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6296526



语音识别 CNN
Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition(2012), Ossama Abdel-Hamid et al.

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6288864



Deep convolutional neural networks for LVCSR(2013), Tara N. Sainath et al. 

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639347



Analysis of CNN-based speech recognition system using raw speech as input(2015), Dimitri Palaz et al. 

https://infoscience.epfl.ch/record/210029/files/Palaz_INTERSPEECH_2015.pdf



Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition(2016), Yanmin Qian et al. 

https://pdfs.semanticscholar.org/8043/cbfed66c98d2255ea79254de620837478099.pdf

 

Very deep multilingual convolutional neural networks for LVCSR(2016), Tom Sercu et al. 

https://arxiv.org/pdf/1509.08967.pdf



Advances in Very Deep Convolutional Neural Networks for LVCSR(2016), Tom Sercu et al. 

https://arxiv.org/pdf/1604.01792.pdf



Deep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention(2016), Dong Yu et al. 

https://pdfs.semanticscholar.org/716e/60cbbdacf01b3148e91a555358a96308b770.pdf?_ga=2.38333155.198966451.1540996486-1278087525.1535180761



语音识别 LSTM
Long short-term memory recurrent neural network architectures for large scale acoustic modeling(2014), Hasim Sak et al. 

https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43905.pdf



Deep LSTM for Large Vocabulary Continuous Speech Recognition(2017), Xu Tian et al. 

https://arxiv.org/pdf/1703.07090.pdf



English Conversational Telephone Speech Recognition by Humans and Machines(2017), George Saon et al. 

https://arxiv.org/pdf/1703.02136.pdf





语音识别 CTC 
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks(2006), Alex Graves et al. 

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.75.6306&rep=rep1&type=pdf



Towards End-to-End Speech Recognition with Recurrent Neural Networks(2014), Alex Graves et al. 

http://proceedings.mlr.press/v32/graves14.pdf



First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs(2014), Andrew L. Maas et al. 

https://arxiv.org/pdf/1408.2873.pdf



Deep Speech: Scaling up end-to-end speech recognition(2014), Awni Y. Hannun et al. 

https://arxiv.org/pdf/1412.5567.pdf



Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification(2015), Kyuyeon Hwang et al. 

https://arxiv.org/pdf/1511.06841.pdf



Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition(2015), Hasim Sak et al.  

https://arxiv.org/pdf/1507.06947.pdf



Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning(2016), Suyoun Kim et al. 

https://arxiv.org/pdf/1609.06773.pdf



Deep Speech 2: End-to-End Speech Recognition in English and Mandarin(2016), Dario Amodei et al. 

http://proceedings.mlr.press/v48/amodei16.pdf



Wav2Letter: an End-to-End ConvNet-based Speech Recognition System(2016), Ronan Collobert et al. 

https://arxiv.org/pdf/1609.03193.pdf



Multi-task Learning with CTC and Segmental CRF for Speech Recognition(2017), Liang Lu et al.

https://arxiv.org/pdf/1702.06378.pdf



Residual Convolutional CTC Networks for Automatic Speech Recognition(2017), Yisen Wang et al.`

https://arxiv.org/pdf/1702.07793.pdf



语音识别 Sequence Transduction
Sequence Transduction with Recurrent Neural Networks(2012), Alex Graves et al. 

https://arxiv.org/pdf/1211.3711.pdf



语音识别 attention
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results(2014), Jan Chorowski et al. 

https://arxiv.org/pdf/1412.1602.pdf



Attention-Based Models for Speech Recognition(2015), Jan Chorowski et al. 

https://arxiv.org/pdf/1506.07503.pdf



End-to-end attention-based large vocabulary speech recognition(2016), Dzmitry Bahdanau et al. 

https://arxiv.org/pdf/1508.04395.pdf



Listen, attend and spell: A neural network for large vocabulary conversational speech recognition(2016), William Chan et al. 

https://arxiv.org/pdf/1508.01211.pdf



End-to-end attention-based distant speech recognition with Highway LSTM(2016), Hassan Taherian. 

https://arxiv.org/pdf/1610.05361.pdf



Direct Acoustics-to-Word Models for English Conversational Speech Recognition(2017), Kartik Audhkhasi et al. 

https://arxiv.org/pdf/1703.07754.pdf



语音识别 多通道 
Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition(2017), Tara N. Sainath et al. 

http://www.ee.columbia.edu/~ronw/pubs/taslp2017-multichannel.pdf



Multichannel End-to-end Speech Recognition(2017), Tsubasa Ochiai et al. 

https://arxiv.org/pdf/1703.04783.pdf



语音合成 SampleRNN
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model(2016), Soroush Mehri et al.

https://arxiv.org/pdf/1612.07837.pdf



语音合成 WaveNet 
WaveNet: A Generative Model for Raw Audio(2016), Aäron van den Oord et al. 

https://arxiv.org/pdf/1609.03499.pdf



语音合成 Deep Voice
Deep Voice: Real-time Neural Text-to-Speech(2017), Sercan O. Arik et al. 

https://arxiv.org/pdf/1702.07825.pdf



语音合成 Deep Voice 2
Deep Voice 2: Multi-Speaker Neural Text-to-Speech(2017), Sercan Arik et al. 

https://arxiv.org/pdf/1705.08947.pdf



语音合成 Tacotron
Tacotron: Towards End-to-End Speech Synthesis(2017), Yuxuan Wang et al. 

https://pdfs.semanticscholar.org/f258/f0d3260e7fbdd961993086aaafa2afc714c9.pdf



语音合成 Tacotron 2
Natural tts synthesis by conditioning wavenet on mel spectrogram predictions(2018), Jonathan Shen et al. 

https://sigport.org/sites/default/files/docs/ICASSP%202018%20-%20Tacotron%202.pdf



语音合成 Voiceloop
Voiceloop: Voice Fitting and Synthesis via a Phonological Loop(2018), Yaniv Taigman et al. 

https://arxiv.org/pdf/1707.06588.pdf





声纹识别 x-vector 使用TDNN提取语音的embedding 
Deep Neural Network Embeddings for Text-Independent Speaker Verification(2017), David Snyder et al.

http://danielpovey.com/files/2017_interspeech_embeddings.pdf



百度 端到端声纹识别 Triplet Loss
Deep Speaker: an End-to-End Neural Speaker Embedding System(2017), Chao Li et al. 

https://arxiv.org/pdf/1705.02304.pdf



声纹识别 3D卷积网络 
Text-independent speaker verification using 3d convolutional neural networks(2018), Amirsina Torfi et al. 

https://arxiv.org/pdf/1705.09422.pdf



声纹识别 端到端 GE2E
Generalized End-to-End Loss for Speaker Verfication(2018)  Wan L et al. 

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8462665



代码


kaldi  

使用广泛的语音工具包 

https://github.com/kaldi-asr/kaldi



A TensorFlow implementation of Baidu's DeepSpeech architecture     

语音识别 Baidu DeepSpeech TensorFlow实现

https://github.com/mozilla/DeepSpeech



Speech-to-Text-WaveNet : End-to-end sentence level English speech recognition based on DeepMind's WaveNet and tensorflow 

语音识别 DeepMind's WaveNet TensorFlow实现

https://github.com/buriburisuri/speech-to-text-wavenet



End-to-end automatic speech recognition system implemented in TensorFlow.

端到端语音识别 TensorFlow实现

https://github.com/zzw922cn/Automatic_Speech_Recognition



A PyTorch Implementation of End-to-End Models for Speech-to-Text 

端到端语音识别 PyTorch实现

https://github.com/awni/speech



A PaddlePaddle implementation of DeepSpeech2 architecture for ASR.

语音识别 DeepSpeech2 PaddlePaddle实现

https://github.com/PaddlePaddle/DeepSpeech



A TensorFlow Implementation of Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model 

语音合成 Tacotron TensorFlow实现

https://github.com/Kyubyong/tacotron



Tacotron 2 - PyTorch implementation with faster-than-realtime inference 

语音合成 Tacotron2 PyTorch实现

https://github.com/NVIDIA/tacotron2



Deep neural networks for voice conversion (voice style transfer) in Tensorflow 

语音合成 Deep-voice TensorFlow实现

https://github.com/andabi/deep-voice-conversion



A method to generate speech across multiple speakers 

语音合成 facebook PyTorch实现

https://github.com/facebookresearch/loop



Speaker embedding(verification and recognition) using Pytorch 

声纹识别 PyTorch实现

https://github.com/qqueing/DeepSpeaker-pytorch



Deep Learning & 3D Convolutional Neural Networks for Speaker Verification 

声纹识别 3D卷积 TensorFlow实现

https://github.com/astofi/3D-convolutional-speaker-recognition



产品应用


百度语音官网 

http://yuyin.baidu.com/



腾讯AI开放平台 

https://ai.qq.com/product/aaiasr.shtml



讯飞开放平台 

https://xfyun.cn/services/voicedictation



必应语音 

https://azure.microsoft.com/zh-cn/services/cognitive-services/speech/



作者简介
刘斌，中科院自动化所博士生，研究方向为鲁棒性声学建模。