4 citations · 13 across the 15 of their papers we have counts for
6 papers · 1 filter
Robust Unsupervised Adaptation of a Speech Recogniser Using Entropy Minimisation and Speaker Codes
Rogier C. van Dalen, Shucong Zhang, Titouan Parcollet +1
Speech recognisers usually perform optimally only in a specific environment and need to be adapted to work well in another. For adaptation to a new speaker, there is often too litt…
Evaluation of LLMs in Speech is Often Flawed: Test Set Contamination in Large Language Models for Speech Recognition
Yuan Tseng, Titouan Parcollet, Rogier van Dalen +2
Recent work suggests that large language models (LLMs) can improve performance of speech tasks compared to existing systems. To support their claims, results on LibriSpeech and Com…
Cross-Attention is all you need: Real-Time Streaming Transformers for Personalised Speech Enhancement
Shucong Zhang, Malcolm Chadwick, Alberto Gil C. P. Ramos +1
Personalised speech enhancement (PSE), which extracts only the speech of a target user and removes everything else from a recorded audio clip, can potentially improve users' experi…
Transformer-based Streaming ASR with Cumulative Attention
Mohan Li, Shucong Zhang, Catalin Zorila +1
In this paper, we propose an online attention mechanism, known as cumulative attention (CA), for streaming Transformer-based automatic speech recognition (ASR). Inspired by monoton…
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers
Shucong Zhang, Cong-Thanh Do, Rama Doddipatla +3
Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset. That is, in gen…
When Can Self-Attention Be Replaced by Feed Forward Layers?
Shucong Zhang, Erfan Loweimi, Peter Bell +1
Recently, self-attention models such as Transformers have given competitive results compared to recurrent neural network systems in speech recognition. The key factor for the outst…