3 papers
cs.SD2024
Speaker Adaptation for Quantised End-to-End ASR Models
Qiuming Zhao, Guangzhi Sun, Chao Zhang +2
End-to-end models have shown superior performance for automatic speech recognition (ASR). However, such models are often very large in size and thus challenging to deploy on resour…
cs.SD2024
SAML: Speaker Adaptive Mixture of LoRA Experts for End-to-End ASR
Qiuming Zhao, Guangzhi Sun, Chao Zhang +2
Mixture-of-experts (MoE) models have achieved excellent results in many tasks. However, conventional MoE models are often very large, making them challenging to deploy on resource-…
cs.SD2023
Enhancing Quantised End-to-End ASR Models via Personalisation
Qiuming Zhao, Guangzhi Sun, Chao Zhang +2
Recent end-to-end automatic speech recognition (ASR) models have become increasingly larger, making them particularly challenging to be deployed on resource-constrained devices. Mo…