Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System
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...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
05.12.2020
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2012.03154 |