Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Evaluating Voice Conversion-based Privacy Protection against Informed
Attackers
summary: Speech signals are a rich source of speaker-related information including
sensitive attributes like identity or accent. With a small amount of found
speech data, such attributes can be extracted and modeled for malicious
purposes like voice cloning, spoofing, etc. In this paper, we investigate
speaker anonymization strategies based on voice conversion. In contrast to
prior evaluations, we argue that different types of attackers can be defined
depending on the extent of their knowledge about the conversion scheme. We
compare two frequency warping-based conversion methods and a deep learning
based method in three attack scenarios. The utility of the converted speech is
measured through the word error rate achieved by automatic speech recognition,
while privacy protection is assessed by state-of-the-art speaker verification
techniques (i-vectors and x-vectors). Our results show that voice conversion
schemes are unable to effectively protect against an attacker that has
extensive knowledge of the type of conversion and how it has been applied, but
may provide some protection against less knowledgeable attackers.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Evaluating Voice Conversion-based Privacy Protection against Informed Attackers
summary: Speech signals are a rich source of speaker-related information including sensitive attributes like identity or accent. With a small amount of found speech data, such attributes can be extracted and modeled for malicious purposes like voice cloning, spoofing, etc. In this paper, we investigate speaker anonymization strategies based on voice conversion. In contrast to prior evaluations, we argue that different types of attackers can be defined depending on the extent of their knowledge about the conversion scheme. We compare two frequency warping-based conversion methods and a deep learning based method in three attack scenarios. The utility of the converted speech is measured through the word error rate achieved by automatic speech recognition, while privacy protection is assessed by state-of-the-art speaker verification techniques (i-vectors and x-vectors). Our results show that voice conversion schemes are unable to effectively protect against an attacker that has extensive knowledge of the type of conversion and how it has been applied, but may provide some protection against less knowledgeable attackers.
id: http://arxiv.org/abs/1911.03934v1
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