Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech Recognition System
<|reference_start|>Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech Recognition System: Multi-talker speech recognition and target-talker speech recognition, both involve transcription in multi-talker contexts, remain significant challenges. However, existing methods rarely attempt to simultaneously address both tasks. In this study, we propose a pioneering approach to empower Whisper, which is a speech foundation model, to tackle joint multi-talker and target-talker speech recognition tasks. Specifically, (i) we freeze Whisper and plug a Sidecar separator into its encoder to separate mixed embedding for multiple talkers; (ii) a Target Talker Identifier is introduced to identify the embedding flow of the target talker on the fly, requiring only three-second enrollment speech as a cue; (iii) soft prompt tuning for decoder is explored for better task adaptation. Our method outperforms previous methods on two- and three-talker LibriMix and LibriSpeechMix datasets for both tasks, and delivers acceptable zero-shot performance on multi-talker ASR on AishellMix Mandarin dataset.<|reference_end|>
@article{meng2024empowering,
    title={Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech
  Recognition System},
    author={Lingwei Meng, Jiawen Kang, Yuejiao Wang, Zengrui Jin, Xixin Wu,
  Xunying Liu, Helen Meng},
    journal={arXiv preprint arXiv:2407.09817},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09817},
    primaryClass={cs.SD cs.CL eess.AS}
}