https://github.com/x-lance/paperreading

整理各研究方向经典论文
https://github.com/x-lance/paperreading

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整理各研究方向经典论文

• Host: GitHub
• URL: https://github.com/x-lance/paperreading
• Owner: X-LANCE
• Created: 2023-12-03T10:34:52.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-04T08:21:17.000Z (over 1 year ago)
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  • Readme: README.md

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README

        
# 各研究方向经典论文

## ASR

- E2E based ASR

  

    CTC: https://www.cs.toronto.edu/~graves/icml_2006.pdf

    

    Attention based ASR: https://arxiv.org/pdf/1508.01211.pdf

    

    CTC+Attention: https://arxiv.org/pdf/1609.06773.pdf

    

    RNN-T: https://arxiv.org/pdf/1211.3711.pdf

    

    RNN-T+: https://arxiv.org/pdf/1303.5778.pdf

    

    Streaming RNN-T: https://arxiv.org/pdf/1811.06621.pdf?fbclid=IwAR3Ya9ZfBNN40UO0wct7dGupjlBFEpU47IRHK-wXmejI4U2UQGf03sXHMlw.I

    

    T-T: https://arxiv.org/abs/2002.02562 ;  https://arxiv.org/abs/1910.12977

    

    Streaming T-T: https://arxiv.org/abs/2010.11395

    

    Factorized T-T: https://arxiv.org/abs/2110.01500

    

    Transducer in Espnet: https://arxiv.org/pdf/2201.05420.pdf

    

- Review

  

    E2E review:  https://arxiv.org/abs/2111.01690

    

- Streaming LAS

  

    Trigger Attention: https://www.merl.com/publications/docs/TR2019-015.pdf

    

    Online and Linear-Time Attention by Enforcing Monotonic Alignments: https://arxiv.org/abs/1704.00784

    

    MoCha: https://arxiv.org/abs/1712.05382

    

    Monotonic Infinite Lookback Attention for Simultaneous Machine Translation”: https://arxiv.org/abs/1906.05218

    

- Latency optimization

  

    Fastemit:  https://arxiv.org/abs/2010.11148

    

- AM/LM Adaptation

  

    Density Ratio: https://arxiv.org/abs/2002.11268  → HAT; ILMT; ILME

    

    Fast adapt: https://arxiv.org/abs/2104.11127

    

    Factorized T-T: https://arxiv.org/abs/2110.01500

    

    MetaAdapter： https://arxiv.org/pdf/2105.11905.pdf

    

    PERSONALIZATION STRATEGIES FOR END-TO-END SPEECH RECOGNITION SYSTEMS: https://arxiv.org/abs/2102.07739

    

    IMPROVED NEURAL LANGUAGE MODEL FUSION FOR STREAMING RECURRENT NEURAL NETWORK TRANSDUCER: https://arxiv.org/abs/2010.13878

    

- Entity Recognition

    Deep context: end-to-end contextual speech recognition: https://arxiv.org/abs/1808.02480

    Contextual Speech Recognition in End-to-End Neural Network Systems using Beam Search: https://www.isca-speech.org/archive_v0/Interspeech_2018/pdfs/2416.pdf

  

    END-TO-END CONTEXTUAL SPEECH RECOGNITION USING CLASS LANGUAGE MODELS AND A TOKEN PASSING DECODER: https://arxiv.org/abs/1812.02142

            

    Contextual Speech Recognition with Difficult Negative Training Examples: https://arxiv.org/abs/1810.12170

            

    Joint Grapheme and Phoneme Embeddings for Contextual End-to-End ASR: https://x-lance.sjtu.edu.cn/papers/2019/zhc00-chen-is2019.pdf

            

    Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models: https://arxiv.org/abs/1906.09292

            

    Shallow-Fusion End-to-End Contextual Biasing: https://www.isca-speech.org/archive_v0/Interspeech_2019/pdfs/1209.pdf

    Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model: https://arxiv.org/abs/2005.07394

            

    Contextual RNN-T for open domain ASR: https://arxiv.org/abs/2006.03411

            

    Rapid RNN-T Adaptation Using Personalized Speech Synthesis and Neural Language Generator: https://indico2.conference4me.psnc.pl/event/35/contributions/3010/attachments/649/680/Mon-3-7-3.pdf

    Class lm and word mapping for contextual biasing in end-to-end asr: https://arxiv.org/abs/2007.05609

            

    Hierarchical Multi-Stage Word-to-Grapheme Named Entity Corrector for Automatic Speech Recognition: https://www.isca-speech.org/archive_v0/Interspeech_2020/pdfs/3174.pdf

        

    Improving proper noun recognition in end-to-end asr by customization of the mwer loss criterion: https://arxiv.org/abs/2005.09756

        

     Deep Shallow Fusion for RNN-T Personalization: https://arxiv.org/abs/2011.07754

        

     A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systems: https://arxiv.org/abs/2108.07493

        

     Improving RNN-T for Domain Scaling Using Semi-Supervised Training with Neural TTS: https://www.isca-speech.org/archive/pdfs/interspeech_2021/deng21_interspeech.pdf

        

     Contextual Density Ratio for Language Model Biasing of Sequence to Sequence ASR Systems: https://www.isca-speech.org/archive/pdfs/interspeech_2021/andresferrer21_interspeech.pdf

        

     End to End Transformer-Based Contextual Speech Recognition Based on Pointer Network: https://www.isca-speech.org/archive/pdfs/interspeech_2021/lin21e_interspeech.pdf

        

     Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition: https://arxiv.org/abs/2110.04891

        

     Instant One-Shot Word-Learning for Context-Specific Neural Sequence-to-Sequence Speech Recognition: https://arxiv.org/abs/2107.02268

        

     Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition: https://arxiv.org/abs/2110.02220

        

     Spell my name: keyword boosted speech recognition: https://arxiv.org/pdf/2110.02791.pdf

        

     Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion: https://arxiv.org/abs/2104.02194

        

     CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition: https://arxiv.org/abs/2012.09466

        

     Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition: https://arxiv.org/abs/2109.00627

    

- MOE

## TTS

* Review

  A Survey on Neural Speech Synthesis: https://arxiv.org/pdf/2106.15561.pdf

* Spectrogram-based TTS

  \[Tacotron2\] Natural tts synthesis by conditioning wavenet on mel spectrogram predictions: https://arxiv.org/pdf/1712.05884.pdf

  

  Fastspeech 2: Fast and high-quality end-to-end text to speech: https://arxiv.org/pdf/2006.04558.pdf

  

  Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search: https://proceedings.neurips.cc/paper/2020/file/5c3b99e8f92532e5ad1556e53ceea00c-Paper.pdf

  

  Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech: http://proceedings.mlr.press/v139/popov21a/popov21a.pdf

  

* End-to-end TTS

  \[VITS\] Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech: http://proceedings.mlr.press/v139/kim21f/kim21f.pdf

  

  NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers: https://arxiv.org/pdf/2304.09116.pdf

  

* VQ-based and GPT-based TTS

  VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature: https://arxiv.org/pdf/2204.00768.pdf

  

  \[VALLE\] Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers: https://arxiv.org/pdf/2301.02111.pdf

  

  \[Spear-TTS\] Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision: https://arxiv.org/pdf/2302.03540.pdf

* Vocoder

  MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis: https://proceedings.neurips.cc/paper/2019/file/6804c9bca0a615bdb9374d00a9fcba59-Paper.pdf

  

  HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis: https://proceedings.neurips.cc/paper/2020/file/c5d736809766d46260d816d8dbc9eb44-Paper.pdf

  

  BigVGAN: A Universal Neural Vocoder with Large-Scale Training: https://arxiv.org/pdf/2206.04658.pdf

  

* Prosody, Emotion and Expressiveness

  TODO