zzw922cn/awesome-speech-recognition-speech-synthesis-papers

awesome-speech-recognition-speech-synthesis-papers

Paper List

• Text-to-Audio
• Automatic Speech Recognition(ASR)
• Speaker Verification
• Voice Conversion(VC)
• Speech Synthesis(TTS)
• Language Modelling
• Confidence Estimates
• Music Modelling
• Interesting papers

Text to Audio

• AudioLM: a Language Modeling Approach to Audio Generation(2022), Zalán Borsos et al. [[pdf]](https://arxiv.org/pdf/2209.03143)
• AudioLDM: Text-to-Audio Generation with Latent Diffusion Models(2023), Haohe Liu et al. [[pdf]](https://arxiv.org/pdf/2301.12503)
• MusicLM: Generating Music From Text(2023), Andrea Agostinelli et al. [[pdf]](https://arxiv.org/pdf/2301.11325)
• Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion(2023), Flavio Schneider et al. [[pdf]](https://arxiv.org/pdf/2301.11757)
• Noise2Music: Text-conditioned Music Generation with Diffusion Models(2023), Qingqing Huang et al. [[pdf]](https://arxiv.org/pdf/2302.03917)

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]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6768244)
• A Maximum Likelihood Approach to Continuous Speech Recognition(1983), LALIT R. BAHL et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4767370&tag=1)
• Heterogeneous Acoustic Measurements and Multiple Classifiers for Speech Recognition(1986), Andrew K. Halberstadt. [[pdf]](https://groups.csail.mit.edu/sls/publications/1998/phdthesis-drew.pdf)
• Maximum Mutual Information Estimation of Hidden Markov Model Parameters for Speech Recognition(1986), Lalit R. Bahi et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1169179)
• A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition(1989), Lawrence R Rabiner. [[pdf]](https://pdfs.semanticscholar.org/fb04/6159dfb4a2beb95756fe1116056a6d922565.pdf?\_ga=2.37020706.362861000.1494045851-921183529.1494045851)
• Phoneme recognition using time-delay neural networks(1989), Alexander H. Waibel et al. [[pdf]](https://pdfs.semanticscholar.org/b554/da42487697cb0d01a4146858e966c1d2404f.pdf?\_ga=2.97032540.235965811.1494658719-1308334183.1494658711)
• Speaker-independent phone recognition using hidden Markov models(1989), Kai-Fu Lee et al. [[pdf]](http://repository.cmu.edu/cgi/viewcontent.cgi?article=2768&context=compsci)
• Hidden Markov Models for Speech Recognition(1991), B. H. Juang et al. [[pdf]](http://www.jstor.org/stable/1268779)
• Review of Tdnn (time Delay Neural Network) Architectures for Speech Recognition(2014), Masahide Sugiyamat et al. [[pdf]](https://pdfs.semanticscholar.org/073b/6128f04fe4b88b88ae297615af289c308753.pdf?\_ga=2.103860032.1725061846.1494658711-1308334183.1494658711)
• Connectionist Speech Recognition: A Hybrid Approach(1994), Herve Bourlard et al. [[pdf]](https://www.researchgate.net/profile/Herve\_Bourlard/publication/230875873\_Connectionist\_Speech\_Recognition\_A\_Hybrid\_Approach/links/0deec5149eb889b8c7000000/Connectionist-Speech-Recognition-A-Hybrid-Approach.pdf)
• A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER)(1997), J.G. Fiscus. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=659110)
• Speech recognition with weighted finite-state transducers(2001), M Mohri et al. [[pdf]](https://cs.nyu.edu/~mohri/pub/hbka.pdf)
• Framewise phoneme classification with bidirectional LSTM and other neural network architectures(2005), Alex Graves et al. [[pdf]](https://pdfs.semanticscholar.org/83d6/1d9b71a838aa150d7ef232dc6d4c73e24250.pdf?\_ga=1.187838062.730356906.1493526584)
• Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks(2006), Alex Graves et al. [[pdf]](https://pdfs.semanticscholar.org/daed/0db4538e1a83b4680545b44e3083843168e7.pdf?\_ga=1.45211874.730356906.1493526584)
• The kaldi speech recognition toolkit(2011), Daniel Povey et al. [[pdf]](http://publications.idiap.ch/downloads/reports/2011/Povey\_Idiap-RR-04-2012.pdf)
• Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition(2012), Ossama Abdel-Hamid et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6288864)
• Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition(2012), George E. Dahl et al. [[pdf]](http://ieeexplore.ieee.org/document/5740583/?part=1)
• Deep Neural Networks for Acoustic Modeling in Speech Recognition(2012), Geoffrey Hinton et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6296526)
• Sequence Transduction with Recurrent Neural Networks(2012), Alex Graves et al. [[pdf]](https://arxiv.org/pdf/1211.3711.pdf)
• Deep convolutional neural networks for LVCSR(2013), Tara N. Sainath et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639347)
• Improving deep neural networks for LVCSR using rectified linear units and dropout(2013), George E. Dahl et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639346)
• Improving low-resource CD-DNN-HMM using dropout and multilingual DNN training(2013), Yajie Miao et al. [[pdf]](https://pdfs.semanticscholar.org/a818/a229c70161d6e46f9861bb5b7d59065d3982.pdf?\_ga=1.187845614.730356906.1493526584)
• Improvements to deep convolutional neural networks for LVCSR(2013), Tara N. Sainath et al. [[pdf]](https://pdfs.semanticscholar.org/b299/c8878276d837f9417eb4760ad7b69edb0b58.pdf?\_ga=1.150662000.730356906.1493526584)
• Machine Learning Paradigms for Speech Recognition: An Overview(2013), Li Deng et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6423821)
• Recent advances in deep learning for speech research at Microsoft(2013), Li Deng et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639345)
• Speech recognition with deep recurrent neural networks(2013), Alex Graves et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6638947)
• Convolutional deep maxout networks for phone recognition(2014), László Tóth et al. [[pdf]](https://pdfs.semanticscholar.org/0a24/5098455a6663f922a83d318f7b61d357ab1f.pdf?\_ga=1.218359519.730356906.1493526584)
• Convolutional Neural Networks for Speech Recognition(2014), Ossama Abdel-Hamid et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6857341)
• Combining time- and frequency-domain convolution in convolutional neural network-based phone recognition(2014), László Tóth. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6853584)
• Deep Speech: Scaling up end-to-end speech recognition(2014), Awni Y. Hannun et al. [[pdf]](https://arxiv.org/pdf/1412.5567.pdf)
• End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results(2014), Jan Chorowski et al. [[pdf]](https://arxiv.org/pdf/1412.1602.pdf)
• First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs(2014), Andrew L. Maas et al. [[pdf]](https://arxiv.org/pdf/1408.2873.pdf)
• Long short-term memory recurrent neural network architectures for large scale acoustic modeling(2014), Hasim Sak et al. [[pdf]](https://pdfs.semanticscholar.org/c85d/46a94768bdcf7ffcb844b47c5b8e8e8234a3.pdf?\_ga=1.8585459.730356906.1493526584)
• Robust CNN-based speech recognition with Gabor filter kernels(2014), Shuo-Yiin Chang et al. [[pdf]](https://pdfs.semanticscholar.org/1d34/0fe19026b0359bde23fcd7299a99a240bd15.pdf?\_ga=1.184683503.730356906.1493526584)
• Stochastic pooling maxout networks for low-resource speech recognition(2014), Meng Cai et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6854204)
• Towards End-to-End Speech Recognition with Recurrent Neural Networks(2014), Alex Graves et al. [[pdf]](https://pdfs.semanticscholar.org/0fa5/53cfa0cf3cbdf7a913aa2ae789a757dfb32f.pdf?\_ga=1.214035281.730356906.1493526584)
• A neural transducer(2015), N Jaitly et al. [[pdf]](https://arxiv.org/abs/1511.04868)
• Attention-Based Models for Speech Recognition(2015), Jan Chorowski et al. [[pdf]](https://pdfs.semanticscholar.org/b624/504240fa52ab76167acfe3156150ca01cf3b.pdf?\_ga=1.50080608.730356906.1493526584)
• Analysis of CNN-based speech recognition system using raw speech as input(2015), Dimitri Palaz et al. [[pdf]](https://pdfs.semanticscholar.org/31f5/36e48482fc273d521525604606f417638881.pdf?\_ga=1.213722706.730356906.1493526584)
• Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks(2015), Tara N. Sainath et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7178838)
• Deep convolutional neural networks for acoustic modeling in low resource languages(2015), William Chan et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7178332)
• Deep Neural Networks for Single-Channel Multi-Talker Speech Recognition(2015), Chao Weng et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7122291)
• EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding(2015), Y Miao et al. [[pdf]](https://arxiv.org/pdf/1507.08240.pdf)
• Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognition(2015), Hasim Sak et al. [[pdf]](https://pdfs.semanticscholar.org/9fca/2af9a0e3f2c5c3ed47abb3ebd21b7265ac2b.pdf?\_ga=1.222094174.730356906.1493526584)
• Lexicon-Free Conversational Speech Recognition with Neural Networks(2015), Andrew L. Maas et al. [[pdf]](https://pdfs.semanticscholar.org/55ee/875b9039febd378a3f8ac4e3d7603f83d57c.pdf?\_ga=2.128588684.1093285980.1494121465-1276580355.1494121465)
• Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification(2015), Kyuyeon Hwang et al. [[pdf]](https://arxiv.org/pdf/1511.06841.pdf)
• Advances in All-Neural Speech Recognition(2016), Geoffrey Zweig et al. [[pdf]](https://arxiv.org/pdf/1609.05935.pdf)
• Advances in Very Deep Convolutional Neural Networks for LVCSR(2016), Tom Sercu et al. [[pdf]](https://pdfs.semanticscholar.org/76b1/791f2d2776c4d3dd671b7e4f2a9fb3575703.pdf?\_ga=1.150210288.730356906.1493526584)
• End-to-end attention-based large vocabulary speech recognition(2016), Dzmitry Bahdanau et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7472618)
• Deep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention(2016), Dong Yu et al. [[pdf]](https://pdfs.semanticscholar.org/8926/fa45d9fc76523766a9d65e2c3b4a9c3feb88.pdf?\_ga=1.37888869.730356906.1493526584)
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin(2016), Dario Amodei et al. [[pdf]](https://pdfs.semanticscholar.org/c2ba/9d550bbfb542e9fdd6e817e9be15585d0f47.pdf?\_ga=1.248137409.730356906.1493526584)
• End-to-end attention-based distant speech recognition with Highway LSTM(2016), Hassan Taherian. [[pdf]](https://arxiv.org/pdf/1610.05361.pdf)
• Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning(2016), Suyoun Kim et al. [[pdf]](https://arxiv.org/pdf/1609.06773.pdf)
• Listen, attend and spell: A neural network for large vocabulary conversational speech recognition(2016), William Chan et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7472621)
• Latent Sequence Decompositions(2016), William Chan et al. [[pdf]](https://arxiv.org/pdf/1610.03035.pdf)
• Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks(2016), Tara N. Sainath et al. [[pdf]](https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/45401.pdf)
• Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition(2016), Suyoun Kim et al. [[pdf]](https://pdfs.semanticscholar.org/b9fc/cd8bee6e6998b87b4efc671dbcee45917282.pdf?\_ga=2.168507874.235965811.1494658719-1308334183.1494658711)
• Segmental Recurrent Neural Networks for End-to-End Speech Recognition(2016), Liang Lu et al. [[pdf]](https://pdfs.semanticscholar.org/8477/ec32bc1dde071bed8174348da5cd6740dab0.pdf?\_ga=1.220546782.730356906.1493526584)
• Towards better decoding and language model integration in sequence to sequence models(2016), Jan Chorowski et al. [[pdf]](https://arxiv.org/pdf/1612.02695.pdf)
• Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition(2016), Yanmin Qian et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7552554)
• Very Deep Convolutional Networks for End-to-End Speech Recognition(2016), Yu Zhang et al. [[pdf]](https://arxiv.org/pdf/1610.03022.pdf)
• Very deep multilingual convolutional neural networks for LVCSR(2016), Tom Sercu et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7472620)
• Wav2Letter: an End-to-End ConvNet-based Speech Recognition System(2016), Ronan Collobert et al. [[pdf]](https://arxiv.org/pdf/1609.03193.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]](https://arxiv.org/pdf/1706.00612)
• An enhanced automatic speech recognition system for Arabic(2017), Mohamed Amine Menacer et al. [[pdf]](https://pdfs.semanticscholar.org/788e/b75befd9c2597f64e072cb2e86f9e7a877e4.pdf?\_ga=1.188540654.730356906.1493526584)
• 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]](https://arxiv.org/pdf/1706.02737)
• A network of deep neural networks for distant speech recognition(2017), Mirco Ravanelli et al. [[pdf]](https://arxiv.org/pdf/1703.08002.pdf)
• An online sequence-to-sequence model for noisy speech recognition(2017), Chung-Cheng Chiu et al. [[pdf]](https://arxiv.org/pdf/1706.06428.pdf)
• An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems(2017), Hany Ahmed et al. [[pdf]](https://pdfs.semanticscholar.org/f5be/2cb9d37e5e54c5d20644ff7025cdee14995f.pdf?\_ga=1.185419759.730356906.1493526584)
• Attention-Based End-to-End Speech Recognition in Mandarin(2017), C Shan et al. [[pdf]](https://arxiv.org/abs/1707.07167)
• Building DNN acoustic models for large vocabulary speech recognition(2017), Andrew L. Maas et al. [[pdf]](https://pdfs.semanticscholar.org/ff7b/9fbbbdc78d874fa93134d643a5a0295f648f.pdf?\_ga=1.242426692.730356906.1493526584)
• Direct Acoustics-to-Word Models for English Conversational Speech Recognition(2017), Kartik Audhkhasi et al. [[pdf]](https://arxiv.org/pdf/1703.07754.pdf)
• Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments(2017), Zixing Zhang et al. [[pdf]](https://arxiv.org/pdf/1705.10874)
• English Conversational Telephone Speech Recognition by Humans and Machines(2017), George Saon et al. [[pdf]](https://arxiv.org/pdf/1703.02136.pdf)
• ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA(2017), Song Han et al. [[pdf]](http://dl.acm.org/citation.cfm?id=3021745)
• Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition(2017), Chris Donahue et al. [[pdf]](https://arxiv.org/pdf/1711.05747)
• Deep LSTM for Large Vocabulary Continuous Speech Recognition(2017), Xu Tian et al. [[pdf]](https://arxiv.org/pdf/1703.07090.pdf)
• Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition(2017), Taesup Kim et al. [[pdf]](https://arxiv.org/pdf/1707.06065v1.pdf)
• Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling(2017), Hairong Liu et al. [[pdf]](https://arxiv.org/pdf/1703.00096.pdf)
• Improving the Performance of Online Neural Transducer Models(2017), Tara N. Sainath et al. [[pdf]](https://arxiv.org/pdf/1712.01807)
• Learning Filterbanks from Raw Speech for Phone Recognition(2017), Neil Zeghidour et al. [[pdf]](https://arxiv.org/pdf/1711.01161)
• Multichannel End-to-end Speech Recognition(2017), Tsubasa Ochiai et al. [[pdf]](https://arxiv.org/pdf/1703.04783.pdf)
• Multi-task Learning with CTC and Segmental CRF for Speech Recognition(2017), Liang Lu et al. [[pdf]](https://arxiv.org/pdf/1702.06378.pdf)
• Multichannel Signal Processing With Deep Neural Networks for Automatic Speech Recognition(2017), Tara N. Sainath et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7859320)
• Multilingual Speech Recognition With A Single End-To-End Model(2017), Shubham Toshniwal et al. [[pdf]](https://arxiv.org/pdf/1711.01694)
• Optimizing expected word error rate via sampling for speech recognition(2017), Matt Shannon. [[pdf]](https://arxiv.org/pdf/1706.02776)
• Residual Convolutional CTC Networks for Automatic Speech Recognition(2017), Yisen Wang et al. [[pdf]](https://arxiv.org/pdf/1702.07793.pdf)
• Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition(2017), Jaeyoung Kim et al. [[pdf]](https://arxiv.org/pdf/1701.03360.pdf)
• Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition(2017), Suyoun Kim et al. [[pdf]](https://pdfs.semanticscholar.org/b9fc/cd8bee6e6998b87b4efc671dbcee45917282.pdf?\_ga=2.162545140.93942331.1493904208-1691509212.1493904208)
• Reducing Bias in Production Speech Models(2017), Eric Battenberg et al. [[pdf]](https://arxiv.org/pdf/1705.04400.pdf)
• Robust Speech Recognition Using Generative Adversarial Networks(2017), Anuroop Sriram et al. [[pdf]](https://arxiv.org/pdf/1711.01567)
• State-of-the-art Speech Recognition With Sequence-to-Sequence Models(2017), Chung-Cheng Chiu et al. [[pdf]](https://arxiv.org/pdf/1712.01769)
• Towards Language-Universal End-to-End Speech Recognition(2017), Suyoun Kim et al. [[pdf]](https://arxiv.org/pdf/1711.02207)
• Accelerating recurrent neural network language model based online speech recognition system(2018), K Lee et al. [[pdf]](https://arxiv.org/pdf/1801.09866)
• An improved hybrid CTC-Attention model for speech recognition(2018), Zhe Yuan et al. [[pdf]](https://arxiv.org/abs/1810.12020)
• Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units(2018), Zhangyu Xiao et al. [[pdf]](https://arxiv.org/abs/1807.04978)
• SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition(2019), Daniel S. Park et al. [[pdf]](https://arxiv.org/pdf/1904.08779.pdf)
• vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations(2019), Alexei Baevski et al. [[pdf]](https://arxiv.org/pdf/1910.05453)
• Effectiveness of self-supervised pre-training for speech recognition(2020), Alexei Baevski et al. [[pdf]](https://arxiv.org/pdf/1911.03912)
• Improved Noisy Student Training for Automatic Speech Recognition(2020), Daniel S. Park, et al. [[pdf]](https://arxiv.org/abs/2005.09629)
• ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context (2020), Wei Han, et al. [[pdf]](https://arxiv.org/abs/2005.03191)
• Conformer: Convolution-augmented Transformer for Speech Recognition(2020), Anmol Gulati, et al. [[pdf]](https://arxiv.org/pdf/2005.08100v1.pdf)
• On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition(2020), Jinyu Li et al. [[pdf]](https://arxiv.org/pdf/2005.14327)
• Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations(2021), Melikasadat Emami et al. [[pdf]](https://arxiv.org/pdf/2112.10950)
• Efficient Training of Audio Transformers with Patchout(2021), Khaled Koutini et al. [[pdf]](https://arxiv.org/pdf/2110.05069)
• MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition(2021), Linghui Meng et al. [[pdf]](https://arxiv.org/pdf/2102.12664)
• Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition(2021), Timo Lohrenz et al. [[pdf]](https://arxiv.org/pdf/2104.00120.pdf)
• SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification(2021), Helin Wang et al. [[pdf]](https://www.isca-speech.org/archive/pdfs/interspeech_2021/wang21d_interspeech.pdf)
• SpecMix: A Mixed Sample Data Augmentation method for Training with Time-Frequency Domain Features(2021), Gwantae Kim et al. [[pdf]](https://www.isca-speech.org/archive/pdfs/interspeech_2021/kim21c_interspeech.pdf)
• The History of Speech Recognition to the Year 2030(2021), Awni Hannun et al. [[pdf]](https://arxiv.org/pdf/2108.00084.pdf)
• Voice Conversion Can Improve ASR in Very Low-Resource Settings(2021), Matthew Baas et al. [[pdf]](https://arxiv.org/pdf/2111.02674)
• Why does CTC result in peaky behavior?(2021), Albert Zeyer et al. [[pdf]](https://arxiv.org/pdf/2105.14849.pdf)
• E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR(2022), W. Ronny Huang et al. [[pdf]](https://arxiv.org/pdf/2204.10749.pdf)
• Music Source Separation with Generative Flow(2022), Ge Zhu et al. [[pdf]](https://arxiv.org/pdf/2204.09079.pdf)
• Improving Self-Supervised Speech Representations by Disentangling Speakers(2022), Kaizhi Qian et al. [[pdf]](https://arxiv.org/pdf/2204.09224.pdf)
• Robust Speech Recognition via Large-Scale Weak Supervision(2022), Alec Radford et al. [[pdf]](https://cdn.openai.com/papers/whisper.pdf)
• On decoder-only architecture for speech-to-text and large language model integration(2023), Jian Wu et al. [[pdf]](https://arxiv.org/pdf/2307.03917.pdf)

Speaker Verification

• Speaker Verification Using Adapted Gaussian Mixture Models(2000), Douglas A.Reynolds et al. [[pdf]](http://www.sciencedirect.com/science/article/pii/S1051200499903615#)
• A tutorial on text-independent speaker verification(2004), Frédéric Bimbot et al. [[pdf]](https://dl.acm.org/ft\_gateway.cfm?id=1289376&ftid=464492&dwn=1&CFID=843437542&CFTOKEN=31448020)
• Deep neural networks for small footprint text-dependent speaker verification(2014), E Variani et al. [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6854363)
• Deep Speaker Vectors for Semi Text-independent Speaker Verification(2015), Lantian Li et al. [[pdf]](https://arxiv.org/pdf/1505.06427)
• Deep Speaker: an End-to-End Neural Speaker Embedding System(2017), Chao Li et al. [[pdf]](https://arxiv.org/pdf/1705.02304.pdf)
• Deep Speaker Feature Learning for Text-independent Speaker Verification(2017), Lantian Li et al. [[pdf]](https://arxiv.org/pdf/1705.03670)
• Deep Speaker Verification: Do We Need End to End?(2017), Dong Wang et al. [[pdf]](https://arxiv.org/pdf/1706.07859)
• Speaker Diarization with LSTM(2017), Quan Wang et al. [[pdf]](https://arxiv.org/pdf/1710.10468)
• Text-Independent Speaker Verification Using 3D Convolutional Neural Networks(2017), Amirsina Torfi et al. [[pdf]](https://arxiv.org/pdf/1705.09422)
• End-to-End Text-Independent Speaker Verification with Triplet Loss on Short Utterances(2017), Chunlei Zhang et al. [[pdf]](https://www.isca-speech.org/archive/Interspeech_2017/pdfs/1608.PDF)
• Deep Neural Network Embeddings for Text-Independent Speaker Verification(2017), David Snyder et al. [[pdf]](https://pdfs.semanticscholar.org/3697/28d7576683a25de8890e4bc02fae6132fccb.pdf)
• Deep Discriminative Embeddings for Duration Robust Speaker Verification(201