Name
Abstract | ||
|---|---|---|
Learning robust speaker embeddings is a crucial step in speaker diarization. Deep neural networks can accurately capture speaker discriminative characteristics and popular deep embeddings such as x-vectors are nowadays a fundamental component of modern diarization systems. Recently, some improvements over the standard TDNN architecture used for x-vectors have been proposed. The ECAPA-TDNN model, for instance, has shown impressive performance in the speaker verification domain, thanks to a carefully designed neural model. In this work, we extend, for the first time, the use of the ECAPA-TDNN model to speaker diarization. Moreover, we improved its robustness with a powerful augmentation scheme that concatenates several contaminated versions of the same signal within the same training batch. The ECAPA-TDNN model turned out to provide robust speaker embeddings under both close-talking and distant-talking conditions. Our results on the popular AMI meeting corpus show that our system significantly outperforms recently proposed approaches. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.21437/Interspeech.2021-941 | Interspeech |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nauman Dawalatabad | 1 | 0 | 0.68 |
| Mirco Ravanelli | 2 | 185 | 17.87 |
| François Grondin | 3 | 7 | 3.92 |
| Jenthe Thienpondt | 4 | 1 | 1.70 |
| Brecht Desplanques | 5 | 26 | 4.45 |
| Hwidong Na | 6 | 0 | 3.04 |