Fine-Tuning Wav2Vec2 for Speaker Recognition
Nik Vaessen, David A. van Leeuwen
2022ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)92 citationsDOIOpen Access PDF
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 cross-entropy or additive angular softmax loss, and an utterance-pair classification variant with BCE loss. Our best performing variant 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 github.com/nikvaessen/w2v2-speaker.
Topics & Concepts
Computer scienceSpeech recognitionSpeaker recognitionSpeech Recognition and SynthesisSpeech and Audio ProcessingMusic and Audio Processing