针对儿童语音特点优化匿名化，提升可懂度与隐私保护
Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models
- 用儿童语料微调自监督模型，适配儿童语音特征
- 在单人与多人场景下，可懂度和听感质量均提升
- 适合需保护儿童语音隐私的教育、医疗等场景
语音匿名化旨在保护说话人身份的同时保留语言内容与语音可用性。然而，现有系统多基于成人语音训练，应用于儿童语音时性能下降。本文通过使用MyST语料库中的儿童语音对基于自监督学习（SSL）的匿名化流程进行领域适配，在单说话人与双说话人混合条件下进行评估。实验表明，儿童领域适配显著提升了语音可懂度与感知质量，同时保持强隐私保护。进一步扩展至多说话人场景发现，结合目标说话人分离与儿童适配的匿名化方法，可在保护隐私的同时保留对话结构。研究强调了儿童专用适配对实际语音匿名系统的重要性。
原文摘要 · Abstract (English)
Voice anonymization aims to protect speaker identity while preserving linguistic content and speech usability. However, most anonymization systems are developed on adult speech, leading to degraded performance when applied to child speech. This paper investigates child-centric anonymization by adapting a self-supervised learning (SSL) based anonymization pipeline to the child speech domain. The system is adapted using child speech from the MyST corpus and evaluated under both single-speaker and two-speaker mixture conditions. Experimental results show that child-domain adaptation improves intelligibility and perceptual quality while maintaining strong privacy protection. Extending the approach to multi-speaker further demonstrates that combining target speaker extraction with child-adapted anonymization provides privacy protection while preserving conversational structure. These findings highlight the importance of child-specific adaptation for practical speech anonymization systems.
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