ECAPA-TDNN Embeddings for Speaker Diarization

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 improvement...

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Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Nauman Dawalatabad, Ravanelli, Mirco, Grondin, François, Thienpondt, Jenthe, Brecht Desplanques, Hwidong Na
Format: Article
Language:English
Subjects:
Artificial neural networks
Machine learning
Robustness
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2104.01466