The CUHK-Tencent Speaker Diarization System for the ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge
Refereed conference paper presented and published in conference proceedings
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摘要This paper describes our speaker diarization system submitted to the Multi-channel Multi-party Meeting Transcription (M2MeT) challenge, where Mandarin meeting data were recorded in multi-channel format for diarization and automatic speech recognition (ASR) tasks. In these meeting scenarios, the uncertainty of the speaker number and the high ratio of overlapped speech present great challenges for diarization. Based on the assumption that there is valuable complementary information between acoustic features, spatial-related and speaker-related features, we propose a multi-level feature fusion mechanism based target-speaker voice activity detection (FFM-TS-VAD) system to improve the performance of the conventional TS-VAD system. Furthermore, we propose a data augmentation method during training to improve the system robustness when the angular difference between two speakers is relatively small. We provide comparisons for different sub-systems we used in M2MeT challenge. Our submission is a fusion of several sub-systems and ranks second in the diarization task.
著者Naijun Zheng, Na Li, Xixin Wu, Lingwei Meng, Jiawen Kang, Haibin Wu, Chao Weng, Dan Su, Helen Meng
會議名稱IEEE International Conference on Acoustics, Speech and Signal Processing
會議開始日07.05.2022
會議完結日13.05.2022
會議地點Singapore
會議國家/地區新加坡
會議論文集題名IEEE International Conference on Acoustics, Speech and Signal Processing
出版年份2022
出版社Institute of Electrical and Electronics Engineers Inc.
頁次9161 - 9165
國際標準書號9781665405409
語言英式英語
關鍵詞feature fusion, M2MeT, multi-channel, overlapped speech, speaker diarization