Robust Speaker Recognition with Transformers Using wav2vec 2.0
Abstract
Recent advances in unsupervised speech representation learning discover new approaches and provide new state-of-the-art for diverse types of speech processing tasks. This paper presents an investigation of using wav2vec 2.0 deep speech representations for the speaker recognition task. The proposed fine-tuning procedure of wav2vec 2.0 with simple TDNN and statistic pooling back-end using additive angular margin loss allows to obtain deep speaker embedding extractor that is well-generalized across different domains. It is concluded that Contrastive Predictive Coding pretraining scheme efficiently utilizes the power of unlabeled data, and thus opens the door to powerful transformer-based speaker recognition systems. The experimental results obtained in this study demonstrate that fine-tuning can be done on relatively small sets and a clean version of data. Using data augmentation during fine-tuning provides additional performance gains in speaker verification. In this study speaker recognition systems were analyzed on a wide range of well-known verification protocols: VoxCeleb1 cleaned test set, NIST SRE 18 development set, NIST SRE 2016 and NIST SRE 2019 evaluation set, VOiCES evaluation set, NIST 2021 SRE, and CTS challenges sets.
Cite
@article{arxiv.2203.15095,
title = {Robust Speaker Recognition with Transformers Using wav2vec 2.0},
author = {Sergey Novoselov and Galina Lavrentyeva and Anastasia Avdeeva and Vladimir Volokhov and Aleksei Gusev},
journal= {arXiv preprint arXiv:2203.15095},
year = {2022}
}
Comments
Submitted to Interspeech2022. arXiv admin note: text overlap with arXiv:2111.02298