Self-supervised speaker embeddings

conference paper

English

Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of leveraging unlabelled utterances, due to the classification loss over training speakers. In this paper, we explore an alternative training strategy to enable the use of unlabelled utterances in training. We propose to train speaker embedding extractors via reconstructing the frames of a target speech segment, given the inferred embedding of another speech segment of the same utterance. We do this by attaching to the standard speaker embedding extractor a decoder network, which we feed not merely with the speaker embedding, but also with the estimated phone sequence of the target frame sequence. The reconstruction loss can be used either as a single objective, or be combined with the standard speaker classification loss. In the latter case, it acts as a regularizer, encouraging generalizability to speakers unseen during training. In all cases, the proposed architectures are trained from scratch and in an endto- end fashion. We demonstrate the benefits from the proposed approach on the VoxCeleb and Speakers in the Wild Databases, and we report notable improvements over the baseline.

speaker recognition, self-supervised learning, deep learning

STAFYLAKIS, T.; ROHDIN, J.; PLCHOT, O.; MIZERA, P.; BURGET, L.

15. 9. 2019

International Speech Communication Association

Graz

1990-9772

Proceedings of Interspeech

2019

9

French Republic

2863

2867

5

https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2842.pdf

stafylakis_is2019_192842.pdf