Speech data carries a range of personal information, such as the speaker's
identity and emotional state. These attributes can be used for malicious
purposes. With the development of virtual assistants, a new generation of
privacy threats has emerged. Current studies have addressed the topic of
preserving speech privacy. One of them, the VoicePrivacy initiative aims to
promote the development of privacy preservation tools for speech technology.
The task selected for the VoicePrivacy 2020 Challenge (VPC) is about speaker
anonymization. The goal is to hide the source speaker's identity while
preserving the linguistic information. The baseline of the VPC makes use of a
voice conversion. This paper studies the impact of the speaker anonymization
baseline system of the VPC on emotional information present in speech
utterances. Evaluation is performed following the VPC rules regarding the
attackers' knowledge about the anonymization system. Our results show that the
VPC baseline system does not suppress speakers' emotions against informed
attackers. When comparing anonymized speech to original speech, the emotion
recognition performance is degraded by 15\% relative to IEMOCAP data, similar
to the degradation observed for automatic speech recognition used to evaluate
the preservation of the linguistic information.

该研究研究了在语音隐私保护的 VoicePrivacy 2020 Challenge 中对说话人匿名化基线系统对语音情感信息的影响，结果表明该系统对知情攻击者未能抑制说话人的情感，且匿名化后的语音相比原始语音情感识别性能下降了 15%，与其保留语言信息的评估方式使用自动语音识别得到的结果类似。

评估说话人匿名在情感语音上的效果

Evaluation of Speaker Anonymization on Emotional Speech

The VoicePrivacy initiative aims to promote the development of privacy
preservation tools for speech technology by gathering a new community to define
the tasks of interest and the evaluation methodology, and benchmarking
solutions through a series of challenges. In this paper, we formulate the voice
anonymization task selected for the VoicePrivacy 2020 Challenge and describe
the datasets used for system development and evaluation. We also present the
attack models and the associated objective and subjective evaluation metrics.
We introduce two anonymization baselines and report objective evaluation
results.

VoicePrivacy 倡导发展语音技术的隐私保护工具，在声音匿名化任务和相关数据集和攻击模型等方面提出挑战，并报告了基于两个匿名化基线的客观评估结果。