5 papers
Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis
Tianyi Xu, Hongjie Chen, Wang Qing +6
Large-scale training corpora have significantly improved the performance of ASR models. Unfortunately, due to the relative scarcity of data, Chinese accents and dialects remain a c…
Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Zhennan Lin, Kaixun Huang, Wei Ren +2
Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as…
Towards Rehearsal-Free Multilingual ASR: A LoRA-based Case Study on Whisper
Tianyi Xu, Kaixun Huang, Pengcheng Guo +4
Pre-trained multilingual speech foundation models, like Whisper, have shown impressive performance across different languages. However, adapting these models to new or specific lan…
U2-KWS: Unified Two-pass Open-vocabulary Keyword Spotting with Keyword Bias
Ao Zhang, Pan Zhou, Kaixun Huang +3
Open-vocabulary keyword spotting (KWS), which allows users to customize keywords, has attracted increasingly more interest. However, existing methods based on acoustic models and p…
Spike-Triggered Contextual Biasing for End-to-End Mandarin Speech Recognition
Kaixun Huang, Ao Zhang, Binbin Zhang +3
The attention-based deep contextual biasing method has been demonstrated to effectively improve the recognition performance of end-to-end automatic speech recognition (ASR) systems…