Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching

• URL: http://arxiv.org/abs/2411.02026v2
• Date: Sun, 10 Aug 2025 04:48:33 GMT
• Title: Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching
• Authors: Yu Pan, Yuguang Yang, Jixun Yao, Lei Ma, Jianjun Zhao,
• Abstract summary: CTEFM-VC is a framework that integrates content-aware timbre ensemble modeling with conditional flow matching.<n>Experiments show CTEFM-VC consistently achieves the best performance in all metrics assessing speaker similarity, speech naturalness, and intelligibility.
• Score: 7.151257248661491
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite recent advances in zero-shot voice conversion (VC), achieving speaker similarity and naturalness comparable to ground-truth recordings remains a significant challenge. In this letter, we propose CTEFM-VC, a zero-shot VC framework that integrates content-aware timbre ensemble modeling with conditional flow matching. Specifically, CTEFM-VC decouples utterances into content and timbre representations and leverages a conditional flow matching model to reconstruct the Mel-spectrogram of the source speech. To enhance its timbre modeling capability and naturalness of generated speech, we first introduce a context-aware timbre ensemble modeling approach that adaptively integrates diverse speaker verification embeddings and enables the effective utilization of source content and target timbre elements through a cross-attention module. Furthermore, a structural similarity-based timbre loss is presented to jointly train CTEFM-VC end-to-end. Experiments show that CTEFM-VC consistently achieves the best performance in all metrics assessing speaker similarity, speech naturalness, and intelligibility, significantly outperforming state-of-the-art zero-shot VC systems.

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