Adolescent suicide is a critical global health issue, and speech provides a cost-effective modality for automatic suicide risk detection. Given the vulnerable population, protecting speaker identity is particularly important, as speech itself can reveal personally identifiable information if the data is leaked or maliciously exploited. This work presents the first systematic study of speaker anonymisation for speech-based suicide risk detection. A broad range of anonymisation methods are investigated, including techniques based on traditional signal processing, neural voice conversion, and speech synthesis. A comprehensive evaluation framework is built to assess the trade-off between protecting speaker identity and preserving information essential for suicide risk detection. Results show that combining anonymisation methods that retain complementary information yields detection performance comparable to that of original speech, while achieving protection of speaker identity for vulnerable populations.

翻译：青少年自杀是一个严峻的全球性健康问题，而语音为自动化的自杀风险检测提供了一种经济有效的模态。考虑到这一人群的脆弱性，保护说话人身份尤为重要，因为一旦数据泄露或被恶意利用，语音本身可能泄露个人身份信息。本研究首次对基于语音的自杀风险检测中的说话人匿名化进行了系统性探讨。我们研究了广泛的匿名化方法，包括基于传统信号处理、神经语音转换以及语音合成的技术。我们构建了一个全面的评估框架，以权衡保护说话人身份与保留自杀风险检测所需关键信息之间的关系。结果表明，结合保留互补信息的匿名化方法，可以在实现对脆弱人群说话人身份保护的同时，获得与原始语音相当的检测性能。