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]

• 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]

• Review of Tdnn (time Delay Neural Network) Architectures for Speech Recognition(2014), Masahide Sugiyamat 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]

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]

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]

• 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]

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

• Neural Voice Cloning with a Few Samples(2018), Sercan O. Arık , Jitong Chen , 1 Kainan Peng , Wei Ping * 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]

Contact Me

For any questions, welcome to send email to :zzw922cn@gmail.com. Thanks!