Name
Title | ||
|---|---|---|
Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for the M2met Challenge. |
Abstract | ||
|---|---|---|
In this paper, we present the speaker diarization system for the Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) from team DKU_DukeECE. As the highly overlapped speech exists in the dataset, we employ an x-vector-based target-speaker voice activity detection (TS-VAD) to find the overlap between speakers. For the single-channel scenario, we separately train a model for each of the 8 channels and fuse the results. We also employ the cross-channel self-attention to further improve the performance, where the non-linear spatial correlations between different channels are learned and fused. Experimental results on the evaluation set show that the single-channel TS-VAD reduces the DER by over 75% from 12.68\% to 3.14%. The multi-channel TS-VAD further reduces the DER by 28% and achieves a DER of 2.26%. Our final submitted system achieves a DER of 2.98% on the AliMeeting test set, which ranks 1st in the M2MET challenge. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICASSP43922.2022.9747019 | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiqing Wang | 1 | 0 | 3.04 |
| Xiaoyi Qin | 2 | 0 | 0.34 |
| Ming Li | 3 | 331 | 17.67 |