Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models

2026-06-29

AI Summary

This work addresses the significant performance degradation of existing voice anonymization systems—primarily designed for adult speech—when applied to children’s voices, which struggle to balance privacy preservation with speech utility. To bridge this gap, the study proposes the first systematic self-supervised learning (SSL)-based anonymization framework tailored specifically for child speech. By performing domain adaptation on the MyST child corpus and extending the approach to both single-speaker and two-speaker mixture scenarios, the method integrates target speaker extraction with explicit modeling of child-specific vocal characteristics. This enables effective identity privacy protection while substantially improving speech intelligibility, perceptual quality, and conversational naturalness, thereby achieving child-centric, high-fidelity voice anonymization.

Abstract

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.

Problem

• voice anonymization
• child speech
• speaker privacy
• speech usability
• multi-speaker

Innovation

• child-centric anonymization
• domain-adapted SSL
• speaker privacy
• multi-speaker speech
• voice anonymization