arXiv:2607.03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Speaker Anonymization Systems (SAS). Existing SAS approaches modify voice characteristics in the hand-crafted feature space or speaker embedding space, often struggling to provide sufficient identity variance across generated voices. In this paper, we propose NouveauVoice, a novel pseudo-speaker generation framework based on a Hierarchical Deep Variational Autoencoder (NVAE). Integrated as a standalon
The rapid advancement of neural speech technologies necessitates the urgent development of robust privacy-preserving measures for voice data.
This development addresses critical privacy risks in the era of advanced AI, impacting personal security, legal frameworks, and ethical AI deployment.
The ability to generate highly varied 'pseudo-speakers' significantly improves voice anonymization techniques beyond existing limited approaches.
- · Privacy advocates
- · Individuals with voice data
- · Cybersecurity firms
- · AI ethics and regulatory bodies
- · Malicious actors using voice impersonation
- · Surveillance technologies reliant on speaker identification
Improved voice anonymization will enhance personal privacy in digital interactions and media.
This technology could reduce the efficacy of voice biometric authentication systems if widely adopted.
The broader application of such robust pseudo-speaker generation might lead to novel forms of content creation or manipulation, requiring new detection methods.
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Read at arXiv cs.AI