12 citations · 14 across the 5 of their papers we have counts for
8 papers
Can Contextual Biasing Remain Effective with Whisper and GPT-2?
Guangzhi Sun, Xianrui Zheng, Chao Zhang +1
End-to-end automatic speech recognition (ASR) and large language models, such as Whisper and GPT-2, have recently been scaled to use vast amounts of training data. Despite the larg…
Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer Generator
Guangzhi Sun, Chao Zhang, Phil Woodland
The incorporation of biasing words obtained through contextual knowledge is of paramount importance in automatic speech recognition (ASR) applications. This paper proposes an innov…
Self-Supervised Learning-Based Source Separation for Meeting Data
Yuang Li, Xianrui Zheng, Philip C. Woodland
Source separation can improve automatic speech recognition (ASR) under multi-party meeting scenarios by extracting single-speaker signals from overlapped speech. Despite the succes…
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition
Xiaoyu Yang, Qiujia Li, Chao Zhang +1
Although large foundation models pre-trained by self-supervised learning have achieved state-of-the-art performance in many tasks including automatic speech recognition (ASR), know…
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax
Keqi Deng, Philip C. Woodland
End-to-end (E2E) automatic speech recognition (ASR) implicitly learns the token sequence distribution of paired audio-transcript training data. However, it still suffers from domai…
Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription
Xianrui Zheng, Chao Zhang, Philip C. Woodland
Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2.0 (W2V2), have become the backbone of many speech tasks. In this paper, to achieve speaker diar…