A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings

Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task. It was shown that single-channel frame-level diarization with serialized output training (SC-FD-SOT), single-channel word-level diarization with SOT (SC-WD-...

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Bibliographic Details
Main Authors Shi, Mohan, Zhang, Jie, Du, Zhihao, Yu, Fan, Chen, Qian, Zhang, Shiliang, Dai, Li-Rong
Format Journal Article
LanguageEnglish
Published 01.11.2022
Subjects
Computer Science - Sound
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Summary:Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task. It was shown that single-channel frame-level diarization with serialized output training (SC-FD-SOT), single-channel word-level diarization with SOT (SC-WD-SOT) and joint training of single-channel target-speaker separation and ASR (SC-TS-ASR) can be exploited to partially solve this problem. In this paper, we propose three corresponding multichannel (MC) SA-ASR approaches, namely MC-FD-SOT, MC-WD-SOT and MC-TS-ASR. For different tasks/models, different multichannel data fusion strategies are considered, including channel-level cross-channel attention for MC-FD-SOT, frame-level cross-channel attention for MC-WD-SOT and neural beamforming for MC-TS-ASR. Results on the AliMeeting corpus reveal that our proposed models can consistently outperform the corresponding single-channel counterparts in terms of the speaker-dependent character error rate.
DOI:10.48550/arxiv.2211.00511