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Computer Science > Sound

arXiv:2109.15053 (cs)
[Submitted on 30 Sep 2021 (v1), last revised 6 May 2022 (this version, v2)]

Title:Fine-tuning wav2vec2 for speaker recognition

Authors:Nik Vaessen, David A. van Leeuwen
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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 this https URL.
Comments: accepted to ICASSP 2022
Subjects: Sound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2109.15053 [cs.SD]
  (or arXiv:2109.15053v2 [cs.SD] for this version)
  https://doi.org/10.48550/arXiv.2109.15053
Related DOI: https://doi.org/10.1109/ICASSP43922.2022.9746952

Submission history

From: Nik Vaessen [view email]
[v1] Thu, 30 Sep 2021 12:16:47 UTC (492 KB)
[v2] Fri, 6 May 2022 09:41:45 UTC (205 KB)
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