Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: One-class learning towards generalized voice spoofing detection
summary: Human voices can be used to authenticate the identity of the speaker, but the
automatic speaker verification (ASV) systems are vulnerable to voice spoofing
attacks, such as impersonation, replay, text-to-speech, and voice conversion.
Recently, researchers developed anti-spoofing techniques to improve the
reliability of ASV systems against spoofing attacks. However, most methods
encounter difficulties in detecting unknown attacks in practical use, which
often have different statistical distributions from known attacks. In this
work, we propose an anti-spoofing system to detect unknown logical access
attacks (i.e., synthetic speech) using one-class learning. The key idea is to
compact the genuine speech representation and inject an angular margin to
separate the spoofing attacks in the embedding space. Our system achieves an
equal error rate of 2.19% on the evaluation set of ASVspoof 2019 Challenge,
outperforming all existing single systems.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: One-class learning towards generalized voice spoofing detection
summary: Human voices can be used to authenticate the identity of the speaker, but the automatic speaker verification (ASV) systems are vulnerable to voice spoofing attacks, such as impersonation, replay, text-to-speech, and voice conversion. Recently, researchers developed anti-spoofing techniques to improve the reliability of ASV systems against spoofing attacks. However, most methods encounter difficulties in detecting unknown attacks in practical use, which often have different statistical distributions from known attacks. In this work, we propose an anti-spoofing system to detect unknown logical access attacks (i.e., synthetic speech) using one-class learning. The key idea is to compact the genuine speech representation and inject an angular margin to separate the spoofing attacks in the embedding space. Our system achieves an equal error rate of 2.19% on the evaluation set of ASVspoof 2019 Challenge, outperforming all existing single systems.
id: http://arxiv.org/abs/2010.13995v1
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