Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Anti-Spoofing Using Transfer Learning with Variational Information
Bottleneck
summary: Recent advances in sophisticated synthetic speech generated from
text-to-speech (TTS) or voice conversion (VC) systems cause threats to the
existing automatic speaker verification (ASV) systems. Since such synthetic
speech is generated from diverse algorithms, generalization ability with using
limited training data is indispensable for a robust anti-spoofing system. In
this work, we propose a transfer learning scheme based on the wav2vec 2.0
pretrained model with variational information bottleneck (VIB) for speech
anti-spoofing task. Evaluation on the ASVspoof 2019 logical access (LA)
database shows that our method improves the performance of distinguishing
unseen spoofed and genuine speech, outperforming current state-of-the-art
anti-spoofing systems. Furthermore, we show that the proposed system improves
performance in low-resource and cross-dataset settings of anti-spoofing task
significantly, demonstrating that our system is also robust in terms of data
size and data distribution.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck
summary: Recent advances in sophisticated synthetic speech generated from text-to-speech (TTS) or voice conversion (VC) systems cause threats to the existing automatic speaker verification (ASV) systems. Since such synthetic speech is generated from diverse algorithms, generalization ability with using limited training data is indispensable for a robust anti-spoofing system. In this work, we propose a transfer learning scheme based on the wav2vec 2.0 pretrained model with variational information bottleneck (VIB) for speech anti-spoofing task. Evaluation on the ASVspoof 2019 logical access (LA) database shows that our method improves the performance of distinguishing unseen spoofed and genuine speech, outperforming current state-of-the-art anti-spoofing systems. Furthermore, we show that the proposed system improves performance in low-resource and cross-dataset settings of anti-spoofing task significantly, demonstrating that our system is also robust in terms of data size and data distribution.
id: http://arxiv.org/abs/2204.01387v1
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