:star2: Speech Recognition Papers :star2:

End-to-End, Listen-Attend-Spell, Speech-Transformer, RNN-Transducer

• [2012/11] Sequence Transduction with Recurrent Neural Networks

• [2014/11] Voice Recognition Using MFCC Algorithm

• [2014/12] Deep Speech: Scaling up end-to-end speech recognition

• [2015/06] Attention-Based Models for Speech-Recognition

• [2015/08] Listen, Attend and Spell

• [2015/12] Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

• [2017/06] Advances in Joint CTC-Attention based E2E ASR with a Deep CNN Encoder and RNN-LM

• [2017/07] Attention Is All You Need

• [2017/12] State-of-the-art Speech Recognition with Sequence-to-Sequence Models

• [2017/12] An Analsis Of Incorporating An External Language Model Into A Sequence-to-Sequence Model

• [2018/04] Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition

• [2019/02] On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition

• [2019/04] SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

• [2019/04] wav2vec: Unsupervised Pre-training for Speech Recognition

• [2019/05] Transformers with convolutional context for ASR

• [2019/08] Korean Grapheme Unit-based Speech Recognition Using Attention-CTC Ensemble Network

• [2019/08] Jasper: An End-to-End Convolutional Neural Acoustic Model

• [2019/11] End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures

• [2019/12] SpecAugment on Large Scale Datasets

• [2020/04] ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for ASR of Contact Centers

• [2020/05] ContextNet: Improving Convolutional Neural Networks for ASR with Global Context

• [2020/05] Conformer: Convolution-augmented Transformer for Speech Recognition

• [2020/06] wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations