保护青少年自杀风险检测中的说话人隐私，同时保持检测效果。
Speaker Anonymisation for Speech-based Suicide Risk Detection
- 融合多种语音匿名技术，保留关键风险信息。
- 匿名后检测性能接近原始语音，误差小于5%。
- 适合隐私敏感的临床与心理健康研究场景。
青少年自杀是全球重大健康问题，语音为自动识别自杀风险提供了低成本手段。由于目标人群脆弱，保护说话人身份尤为重要，因语音本身可能泄露个人身份信息。本文首次系统研究了面向语音自杀风险检测的说话人匿名化方法，评估了基于传统信号处理、神经语音转换和语音合成等多种技术的组合效果。构建了综合评估框架，权衡身份保护与风险检测信息保留之间的关系。结果表明，采用互补性信息保留的匿名化方法组合，可使检测性能接近原始语音水平（性能下降小于5%），同时有效保护脆弱群体的说话人身份。
原文摘要 · Abstract (English)
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.
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