首次研究多语混用语音的匿名化，提升隐私保护能力。
First Steps Towards Voice Anonymization for Code-Switching Speech
- 针对多语混用语音设计专用匿名化模型
- 多语模型在隐私与语音可用性上表现最佳
- 适合语音隐私保护与跨语言研究者
语音匿名化旨在修改音频以隐藏说话人真实身份。现有研究多集中于单一英语朗读数据集，对其他语音类型的有效性尚不明确。本文首次探索多语混用语音的匿名化问题，构建了两个相关语料库，并对多语匿名化模型进行适配，使其适用于该类语音。在两个数据集上测试多语系统及两种语言无关方法的性能，发现仅多语系统在隐私保护与语音保真度方面表现良好。此外，由于语音自发性强及多语语音识别模型对混用支持有限，评估语音可用性面临挑战。
原文摘要 · Abstract (English)
The goal of voice anonymization is to modify an audio such that the true identity of its speaker is hidden. Research on this task is typically limited to the same English read speech datasets, thus the efficacy of current methods for other types of speech data remains unknown. In this paper, we present the first investigation of voice anonymization for the multilingual phenomenon of code-switching speech. We prepare two corpora for this task and propose adaptations to a multilingual anonymization model to make it applicable for code-switching speech. By testing the anonymization performance of this and two language-independent methods on the datasets, we find that only the multilingual system performs well in terms of privacy and utility preservation. Furthermore, we observe challenges in performing utility evaluations on this data because of its spontaneous character and the limited code-switching support by the multilingual speech recognition model.
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