Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models
arXiv:2606.29897v1 Announce Type: cross 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 resul
The increasing sophistication and widespread application of AI in voice processing necessitates advanced anonymization techniques, especially as foundational models are being adapted for diverse populations like children.
Protecting the identities of vulnerable populations like children in audio data is crucial for ethical AI development, privacy regulations, and trust in AI-powered applications, while also unlocking new forms of child-centric AI interaction.
The ability to effectively anonymize child speech, making it safer to collect and utilize such data for AI training and applications without compromising identity.
- · AI developers
- · Child privacy advocates
- · Educational technology sector
- · Healthcare providers for children
- · Malicious actors accessing child identifiers
- · Systems reliant on speaker identification in child audio
Improved ethical guidelines and privacy features for AI systems interacting with children.
Increased collection and utilization of child speech data for various AI applications, such as personalized learning or therapy.
The potential for AI to autonomously interact with children in sensitive contexts while actively maintaining their anonymity and privacy.
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Read at arXiv cs.AI