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Speech synthesis, voice conversion, self-supervised learning, music generation,Automatic Speech Recognition, Speaker Verification, Speech Synthesis, Language Modeling
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README.md

README.md

awesome-speech-recognition-speech-synthesis-papers

automatic speech recognition/speech synthesis paper roadmap, including HMM, DNN, RNN, CNN, Seq2Seq, Attention

Introduction

Automatic Speech Recognition has been investigated for several decades, and speech recognition models are from HMM-GMM to deep neural networks today. It's very necessary to see the history of speech recognition by this awesome paper roadmap. I will cover papers from traditional models to nowadays popular models, not only acoustic models or ASR systems, but also many interesting language models.

Paper List

Automatic Speech Recognition

  • An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition(1982), S. E. LEVINSON et al. [pdf]

  • A Maximum Likelihood Approach to Continuous Speech Recognition(1983), LALIT R. BAHL et al. [pdf]

  • Heterogeneous Acoustic Measurements and Multiple Classifiers for Speech Recognition(1986), Andrew K. Halberstadt. [pdf]

  • Maximum Mutual Information Estimation of Hidden Markov Model Parameters for Speech Recognition(1986), Lalit R. Bahi et al. [pdf]

  • A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition(1989), Lawrence R Rabiner. [pdf]

  • Phoneme recognition using time-delay neural networks(1989), Alexander H. Waibel et al. [pdf]

  • Speaker-independent phone recognition using hidden Markov models(1989), Kai-Fu Lee et al. [pdf]

  • Hidden Markov Models for Speech Recognition(1991), B. H. Juang et al. [pdf]

  • Review of Tdnn (time Delay Neural Network) Architectures for Speech Recognition(2014), Masahide Sugiyamat et al. [pdf]

  • Connectionist Speech Recognition: A Hybrid Approach(1994), Herve Bourlard et al. [pdf]

  • A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER)(1997), J.G. Fiscus. [pdf]

  • Speech recognition with weighted finite-state transducers(2001), M Mohri et al. [pdf]

  • Framewise phoneme classification with bidirectional LSTM and other neural network architectures(2005), Alex Graves et al. [pdf]

  • Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks(2006), Alex Graves et al. [pdf]

  • The kaldi speech recognition toolkit(2011), Daniel Povey et al. [pdf]

  • Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition(2012), Ossama Abdel-Hamid et al. [pdf]

  • Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition(2012), George E. Dahl et al. [pdf]

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

  • Sequence Transduction with Recurrent Neural Networks(2012), Alex Graves et al. [pdf]

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

  • Improving deep neural networks for LVCSR using rectified linear units and dropout(2013), George E. Dahl et al. [pdf]

  • Improving low-resource CD-DNN-HMM using dropout and multilingual DNN training(2013), Yajie Miao et al. [pdf]

  • Improvements to deep convolutional neural networks for LVCSR(2013), Tara N. Sainath et al. [pdf]

  • Machine Learning Paradigms for Speech Recognition: An Overview(2013), Li Deng et al. [pdf]

  • Recent advances in deep learning for speech research at Microsoft(2013), Li Deng et al. [pdf]

  • Speech recognition with deep recurrent neural networks(2013), Alex Graves et al. [pdf]

  • Convolutional deep maxout networks for phone recognition(2014), László Tóth et al. [pdf]

  • Convolutional Neural Networks for Speech Recognition(2014), Ossama Abdel-Hamid et al. [pdf]

  • Combining time- and frequency-domain convolution in convolutional neural network-based phone recognition(2014), László Tóth. [pdf]

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

  • End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results(2014), Jan Chorowski et al. [pdf]

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

  • Long short-term memory recurrent neural network architectures for large scale acoustic modeling(2014), Hasim Sak et al. [pdf]

  • Robust CNN-based speech recognition with Gabor filter kernels(2014), Shuo-Yiin Chang et al. [pdf]

  • Stochastic pooling maxout networks for low-resource speech recognition(2014), Meng Cai et al. [pdf]

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

  • A neural transducer(2015), N Jaitly et al. [pdf]

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

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

  • Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks(2015), Tara N. Sainath et al. [pdf]

  • Deep convolutional neural networks for acoustic modeling in low resource languages(2015), William Chan et al. [pdf]

  • Deep Neural Networks for Single-Channel Multi-Talker Speech Recognition(2015), Chao Weng et al. [pdf]

  • EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding(2015), Y Miao et al. [pdf]

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

  • Lexicon-Free Conversational Speech Recognition with Neural Networks(2015), Andrew L. Maas et al. [pdf]

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

  • Advances in All-Neural Speech Recognition(2016), Geoffrey Zweig et al. [pdf]

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

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

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

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

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

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

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

  • Latent Sequence Decompositions(2016), William Chan et al. [pdf]

  • Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks(2016), Tara N. Sainath et al. [pdf]

  • Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition(2016), Suyoun Kim et al. [pdf]

  • Segmental Recurrent Neural Networks for End-to-End Speech Recognition(2016), Liang Lu et al. [pdf]

  • Towards better decoding and language model integration in sequence to sequence models(2016), Jan Chorowski et al. [pdf]

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

  • Very Deep Convolutional Networks for End-to-End Speech Recognition(2016), Yu Zhang et al. [pdf]

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

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

  • WaveNet: A Generative Model for Raw Audio(2016), Aäron van den Oord et al. [pdf]

  • Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech(2017), Michael Neumann et al. [pdf]

  • An enhanced automatic speech recognition system for Arabic(2017), Mohamed Amine Menacer et al. [pdf]

  • Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM(2017), Takaaki Hori et al. [pdf]

  • A network of deep neural networks for distant speech recognition(2017), Mirco Ravanelli et al. [pdf]

  • An online sequence-to-sequence model for noisy speech recognition(2017), Chung-Cheng Chiu et al. [pdf]

  • An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems(2017), Hany Ahmed et al. [pdf]

  • Attention-Based End-to-End Speech Recognition in Mandarin(2017), C Shan et al. [pdf]

  • Building DNN acoustic models for large vocabulary speech recognition(2017), Andrew L. Maas et al. [pdf]

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

  • Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments(2017), Zixing Zhang et al. [pdf]

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

  • ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA(2017), Song Han et al. [pdf]

  • Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition(2017), Chris Donahue et al. [pdf]

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

  • Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition(2017), Taesup Kim et al. [pdf]

  • Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling(2017), Hairong Liu et al. [pdf]

  • Improving the Performance of Online Neural Transducer Models(2017), Tara N. Sainath et al. [pdf]

  • Learning Filterbanks from Raw Speech for Phone Recognition(2017), Neil Zeghidour et al. [pdf]

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

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

  • Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition(2017), Tara N. Sainath et al. [pdf]

  • Multilingual Speech Recognition With A Single End-To-End Model(2017), Shubham Toshniwal et al. [pdf]

  • Optimizing expected word error rate via sampling for speech recognition(2017), Matt Shannon. [pdf]

  • Residual Convolutional CTC Networks for Automatic Speech Recognition(2017), Yisen Wang et al. [pdf]

  • Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition(2017), Jaeyoung Kim et al. [pdf]

  • Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition(2017), Suyoun Kim et al. [pdf]

  • Reducing Bias in Production Speech Models(2017), Eric Battenberg et al. [pdf]

  • Robust Speech Recognition Using Generative Adversarial Networks(2017), Anuroop Sriram et al. [pdf]

  • State-of-the-art Speech Recognition With Sequence-to-Sequence Models(2017), Chung-Cheng Chiu et al. [pdf]

  • Towards Language-Universal End-to-End Speech Recognition(2017), Suyoun Kim et al. [pdf]

  • Accelerating recurrent neural network language model based online speech recognition system(2018), K Lee et al. [pdf]

  • An improved hybrid CTC-Attention model for speech recognition(2018), Zhe Yuan et al. [pdf]

  • Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units(2018), Zhangyu Xiao et al. [pdf]

Speaker Verification

  • Speaker Verification Using Adapted Gaussian Mixture Models(2000), Douglas A.Reynolds et al. [pdf]

  • A tutorial on text-independent speaker verification(2004), Frédéric Bimbot et al. [pdf]

  • Deep neural networks for small footprint text-dependent speaker verification(2014), E Variani et al. [pdf]

  • Deep Speaker Vectors for Semi Text-independent Speaker Verification(2015), Lantian Li et al. [pdf]

  • Deep Speaker: an End-to-End Neural Speaker Embedding System(2017), Chao Li et al. [pdf]

  • Deep Speaker Feature Learning for Text-independent Speaker Verification(2017), Lantian Li et al. [pdf]

  • Deep Speaker Verification: Do We Need End to End?(2017), Dong Wang et al. [pdf]

  • Speaker Diarization with LSTM(2017), Quan Wang et al. [pdf]

  • Text-Independent Speaker Verification Using 3D Convolutional Neural Networks(2017), Amirsina Torfi et al. [pdf]

Voice Conversion

  • Voice conversion using deep Bidirectional Long Short-Term Memory based Recurrent Neural Networks(2015), Lifa Sun et al. [pdf]
  • Phonetic posteriorgrams for many-to-one voice conversion without parallel data training(2016), Lifa Sun et al. [pdf]
  • AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss(2019), Kaizhi Qian et al. [pdf]
  • Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion(2019), Andy T. Liu et al. [pdf]

Speech Synthesis

  • Signal estimation from modified short-time Fourier transform(1993), Daniel W. Griffin et al. [pdf]

  • Text-to-speech synthesis(2009), Paul Taylor et al. [pdf]

  • A fast Griffin-Lim algorithm(2013), Nathanael Perraudin et al. [pdf]

  • TTS synthesis with bidirectional LSTM based recurrent neural networks(2014), Yuchen Fan et al. [pdf]

  • First Step Towards End-to-End Parametric TTS Synthesis: Generating Spectral Parameters with Neural Attention(2016), Wenfu Wang et al. [pdf]

  • Recent Advances in Google Real-Time HMM-Driven Unit Selection Synthesizer(2016), Xavi Gonzalvo et al. [pdf]

  • SampleRNN: An Unconditional End-to-End Neural Audio Generation Model(2016), Soroush Mehri et al. [pdf]

  • WaveNet: A Generative Model for Raw Audio(2016), Aäron van den Oord et al. [pdf]

  • Char2Wav: End-to-end speech synthesis(2017), J Sotelo et al. [pdf]

  • Deep Voice: Real-time Neural Text-to-Speech(2017), Sercan O. Arik et al. [pdf]

  • Deep Voice 2: Multi-Speaker Neural Text-to-Speech(2017), Sercan Arik et al. [pdf]

  • Deep Voice 3: 2000-Speaker Neural Text-to-speech(2017), Wei Ping et al. [pdf]

  • Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions(2017), Jonathan Shen et al. [pdf]

  • Parallel WaveNet: Fast High-Fidelity Speech Synthesis(2017), Aaron van den Oord et al. [pdf]

  • Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework(2017), S Yang et al. [pdf]

  • Tacotron: Towards End-to-End Speech Synthesis(2017), Yuxuan Wang et al. [pdf]

  • Uncovering Latent Style Factors for Expressive Speech Synthesis(2017), Yuxuan Wang et al. [pdf]

  • VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop(2017), Yaniv Taigman et al. [pdf]

  • ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech(2018), Wei Ping et al. [pdf]

  • Deep Feed-forward Sequential Memory Networks for Speech Synthesis(2018), Mengxiao Bi et al. [pdf]

  • LPCNet: Improving Neural Speech Synthesis Through Linear Prediction(2018), Jean-Marc Valin et al. [pdf]

  • Learning latent representations for style control and transfer in end-to-end speech synthesis(2018), Ya-Jie Zhang et al. [pdf]

  • Neural Voice Cloning with a Few Samples(2018), Sercan O. Arık et al. [pdf]

  • Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis(2018), Y Wang et al. [pdf]

  • Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron(2018), RJ Skerry-Ryan et al. [pdf]

  • DurIAN: Duration Informed Attention Network For Multimodal Synthesis(2019), Chengzhu Yu et al. [pdf]

  • Fast spectrogram inversion using multi-head convolutional neural networks(2019), SÖ Arık et al. [pdf]

  • FastSpeech: Fast, Robust and Controllable Text to Speech(2019), Yi Ren et al. [pdf]

  • Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning(2019), Yu Zhang et al. [pdf]

  • MelNet: A Generative Model for Audio in the Frequency Domain(2019), Sean Vasquez et al. [pdf]

  • Multi-Speaker End-to-End Speech Synthesis(2019), Jihyun Park et al. [pdf]

  • MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis(2019), Kundan Kumar et al. [pdf]

  • Parallel Neural Text-to-Speech(2019), Kainan Peng et al. [pdf]

  • Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN(2019), David Alvarez et al. [pdf]

  • Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS(2019), Mutian He et al. [pdf]

  • Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models(2019), Wei Fang et al. [pdf]

  • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis(2019), Ye Jia et al. [pdf]

  • Waveglow: A flow-based generative network for speech synthesis(2019), R Prenger et al. [pdf]

Language Modelling

  • Class-Based n-gram Models of Natural Language(1992), Peter F. Brown et al. [pdf]

  • An empirical study of smoothing techniques for language modeling(1996), Stanley F. Chen et al. [pdf]

  • A Neural Probabilistic Language Model(2000), Yoshua Bengio et al. [pdf]

  • A new statistical approach to Chinese Pinyin input(2000), Zheng Chen et al. [pdf]

  • Discriminative n-gram language modeling(2007), Brian Roark et al. [pdf]

  • Neural Network Language Model for Chinese Pinyin Input Method Engine(2015), S Chen et al. [pdf]

  • Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition(2016), Xie Chen et al. [pdf]

  • Exploring the limits of language modeling(2016), R Jozefowicz et al. [pdf]

  • On the State of the Art of Evaluation in Neural Language Models(2016), G Melis et al. [pdf]

Music Modelling

  • Onsets and Frames: Dual-Objective Piano Transcription(2017), Curtis Hawthorne et al. [pdf]

Interesting papers

  • Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks(2019), Santiago Pascual et al. [pdf]

  • Self-supervised audio representation learning for mobile devices(2019), Marco Tagliasacchi et al. [pdf]

  • Audio2Face: Generating Speech/Face Animation from Single Audio with Attention-Based Bidirectional LSTM Networks(2019), Guanzhong Tian et al. [pdf]

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