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DDTSE: Discriminative Diffusion Model for Target Speech Extraction
Leying Zhang, Yao Qian, Linfeng Yu +5
Diffusion models have gained attention in speech enhancement tasks, providing an alternative to conventional discriminative methods. However, research on target speech extraction u…
Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
Shaoshi Ling, Yuxuan Hu, Shuangbei Qian +5
Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions. Pretrained large language models (…
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers
Chenda Li, Yao Qian, Zhuo Chen +5
State-of-the-art large-scale universal speech models (USMs) show a decent automatic speech recognition (ASR) performance across multiple domains and languages. However, it remains…
Code-Switching Text Generation and Injection in Mandarin-English ASR
Haibin Yu, Yuxuan Hu, Yao Qian +7
Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance. Automatic Speech Recognition (ASR) with End-to-End (E2E) modeling f…
Target Sound Extraction with Variable Cross-modality Clues
Chenda Li, Yao Qian, Zhuo Chen +5
Automatic target sound extraction (TSE) is a machine learning approach to mimic the human auditory perception capability of attending to a sound source of interest from a mixture o…