awesome-speech-recognition-papers

automatic speech recognition 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]

• 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]

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

• 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]

• 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]

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

• Listen, Attend and Spell(2015), William Chan 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]

• 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]

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

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

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

• 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]

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

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

• 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]

Speech Synthesis

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

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

Language Modelling

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