Fine-tuning wav2vec2 for speaker recognition. (arXiv:2109.15053v2 [cs.SD] UPDATED)

Web: http://arxiv.org/abs/2109.15053

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 …

arxiv fine-tuning