Author

Ming Li

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Preprint Oct 2026

What Makes Codec Tokens Effective for Target Speaker Extraction? A Semantic-Acoustic Disentanglement Perspective

Target speaker extraction (TSE) aims to recover a target speaker's speech from a speech mixture using an enrollment utterance. Recent codec-based generative approaches formulate TSE as conditional speech token generation and achieve promising perceptual quality. However, most existing systems rely on unified codec toke...

Wang Xiang, Bang Zeng, Ming Li · 0 citations

Preprint Oct 2026

Voice Anonymization Made Simple: Training-Free Anonymization with Projected Classifier-Free Guidance

A training-free, two-stage anonymization method for pretrained flow-matching voice conversion systems that improves speaker privacy while maintaining competitive word error rate and emotion recognition performance.

Xiang Shi, Han Zhu, Ming Li et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Exploiting Acoustic and Content-Oriented Speaker Verification Attacks Against Multilingual Voice Anonymization

Attacker ASV systems for voice anonymization have been studied primarily in English, leaving their behavior in multilingual settings largely unexplored. Conventional ASV has shown that both acoustic and contextual information are important for multilingual speaker verification. Inspired by this, we investigate whether...

Ridwan Arefeen, Ze Li, Rong Tong et al. · 0 citations