96 citations · 282 across the 13 of their papers we have counts for
13 papers
Deploying self-supervised learning in the wild for hybrid automatic speech recognition
Mostafa Karimi, Changliang Liu, Kenichi Kumatani +3
Self-supervised learning (SSL) methods have proven to be very successful in automatic speech recognition (ASR). These great improvements have been reported mostly based on highly c…
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data
Junyi Ao, Ziqiang Zhang, Long Zhou +7
This paper studies a novel pre-training technique with unpaired speech data, Speech2C, for encoder-decoder based automatic speech recognition (ASR). Within a multi-task learning fr…
Self-Supervised Learning for speech recognition with Intermediate layer supervision
Chengyi Wang, Yu Wu, Sanyuan Chen +4
Recently, pioneer work finds that speech pre-trained models can solve full-stack speech processing tasks, because the model utilizes bottom layers to learn speaker-related informat…
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
Heming Wang, Yao Qian, Xiaofei Wang +6
Noise robustness is essential for deploying automatic speech recognition (ASR) systems in real-world environments. One way to reduce the effect of noise interference is to employ a…
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes
Rimita Lahiri, Kenichi Kumatani, Eric Sun +1
Multilingual end-to-end(E2E) models have shown a great potential in the expansion of the language coverage in the realm of automatic speech recognition(ASR). In this paper, we aim…
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
Junyi Ao, Rui Wang, Long Zhou +11
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores th…
Large-scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification
Zhengyang Chen, Sanyuan Chen, Yu Wu +5
The speech representations learned from large-scale unlabeled data have shown better generalizability than those from supervised learning and thus attract a lot of interest to be a…
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition
Yiming Wang, Jinyu Li, Heming Wang +3
The goal of self-supervised learning (SSL) for automatic speech recognition (ASR) is to learn good speech representations from a large amount of unlabeled speech for the downstream…
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset
Chengyi Wang, Yu Wu, Shujie Liu +4
Recently, there has been a vast interest in self-supervised learning (SSL) where the model is pre-trained on large scale unlabeled data and then fine-tuned on a small labeled datas…
Speech-language Pre-training for End-to-end Spoken Language Understanding
Yao Qian, Ximo Bian, Yu Shi +4
End-to-end (E2E) spoken language understanding (SLU) can infer semantics directly from speech signal without cascading an automatic speech recognizer (ASR) with a natural language…
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
Chengyi Wang, Yu Wu, Yao Qian +5
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC le…
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding
Peilu Wang, Yao Qian, Frank K. Soong +2
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or…
Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network
Peilu Wang, Yao Qian, Frank K. Soong +2
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e.g. speech utterances or handwritten doc…