Fine-tuning wav2vec2 for speaker recognition
Sound
2022-05-09 v2 Machine Learning
Audio and Speech Processing
Abstract
This paper explores applying the wav2vec2 framework to speaker recognition instead of speech recognition. We study the effectiveness of the pre-trained weights on the speaker recognition task, and how to pool the wav2vec2 output sequence into a fixed-length speaker embedding. To adapt the framework to speaker recognition, we propose a single-utterance classification variant with CE or AAM softmax loss, and an utterance-pair classification variant with BCE loss. Our best performing variant, w2v2-aam, achieves a 1.88% EER on the extended voxceleb1 test set compared to 1.69% EER with an ECAPA-TDNN baseline. Code is available at https://github.com/nikvaessen/w2v2-speaker.
Cite
@article{arxiv.2109.15053,
title = {Fine-tuning wav2vec2 for speaker recognition},
author = {Nik Vaessen and David A. van Leeuwen},
journal= {arXiv preprint arXiv:2109.15053},
year = {2022}
}
Comments
accepted to ICASSP 2022