Fine-tuning wav2vec2 for speaker recognition

• URL: http://arxiv.org/abs/2109.15053v1
• Date: Thu, 30 Sep 2021 12:16:47 GMT
• Title: Fine-tuning wav2vec2 for speaker recognition
• Authors: Nik Vaessen, David A. van Leeuwen
• Abstract summary: 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.
• Score: 3.69563307866315
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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