978 citations · 1.5k across the 58 of their papers we have counts for
6 papers · 1 filter
UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training
Sanyuan Chen, Yu Wu, Chengyi Wang +8
Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness…
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…
Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition
Zhong Meng, Yashesh Gaur, Naoyuki Kanda +4
Text-only adaptation of an end-to-end (E2E) model remains a challenging task for automatic speech recognition (ASR). Language model (LM) fusion-based approaches require an addition…
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…
Template-Based Named Entity Recognition Using BART
Leyang Cui, Yu Wu, Jian Liu +2
There is a recent interest in investigating few-shot NER, where the low-resource target domain has different label sets compared with a resource-rich source domain. Existing method…