Fine-tuning wav2vec2 for speaker recognition

09/30/2021

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by   Nik Vaessen, et al.

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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 extended voxceleb1 test set compared to 1.69 Code is available at https://github.com/nikvaessen/w2v2-speaker.