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arXiv · 2411.02026

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

Yu Pan·Yuguang Yang·Jixun Yao·Lei Ma·Jianjun Zhao
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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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Yu Pan, Yuguang Yang, Jixun Yao, Lei Ma, Jianjun Zhao. 2024-11-04. Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching. https://arxiv.org/abs/2411.02026

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