Name
Abstract | ||
|---|---|---|
Speaker diarization systems are challenged by a trade-off between the temporal resolution and the fidelity of the speaker representation. By obtaining a superior temporal resolution with an enhanced accuracy, a multi-scale approach is a way to cope with such a trade-off. In this paper, we propose a more advanced multi-scale diarization system based on a multi-scale diarization decoder. There are two main contributions in this study that significantly improve the diarization performance. First, we use multi-scale clustering as an initialization to estimate the number of speakers and obtain the average speaker representation vector for each speaker and each scale. Next, we propose the use of 1-D convolutional neural networks that dynamically determine the importance of each scale at each time step. To handle a variable number of speakers and overlapping speech, the proposed system can estimate the number of existing speakers. Our proposed system achieves a state-of-art performance on the CALLHOME and AMI MixHeadset datasets, with 3.92% and 1.05% diarization error rates, respectively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.21437/INTERSPEECH.2022-991 | Conference of the International Speech Communication Association (INTERSPEECH) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae Jin Park | 1 | 6 | 3.15 |
| Nithin Rao Koluguri | 2 | 0 | 1.01 |
| Jagadeesh Balam | 3 | 1 | 1.37 |
| Boris Ginsburg | 4 | 5 | 4.26 |