Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System
Abstract
Spoofing attacks posed by generating artificial speech can severely degrade the performance of a speaker verification system. Recently, many anti-spoofing countermeasures have been proposed for detecting varying types of attacks from synthetic speech to replay presentations. While there are numerous effective defenses reported on standalone anti-spoofing solutions, the integration for speaker verification and spoofing detection systems has obvious benefits. In this paper, we propose a spoofing-robust automatic speaker verification (SR-ASV) system for diverse attacks based on a multi-task learning architecture. This deep learning based model is jointly trained with time-frequency representations from utterances to provide recognition decisions for both tasks simultaneously. Compared with other state-of-the-art systems on the ASVspoof 2017 and 2019 corpora, a substantial improvement of the combined system under different spoofing conditions can be obtained.
Keywords
Cite
@article{arxiv.2012.03154,
title = {Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System},
author = {Yuanjun Zhao and Roberto Togneri and Victor Sreeram},
journal= {arXiv preprint arXiv:2012.03154},
year = {2020}
}
Comments
12 pages, 6 figures, codes used in the experimental section can be found at https://github.com/zhaoyj1122/SRASV