Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909729807138816 |
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| author | Pan, Yu Yang, Yuguang Yao, Jixun Ma, Lei Zhao, Jianjun |
| author_facet | Pan, Yu Yang, Yuguang Yao, Jixun Ma, Lei Zhao, Jianjun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_02026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching Pan, Yu Yang, Yuguang Yao, Jixun Ma, Lei Zhao, Jianjun Sound Artificial Intelligence Audio and Speech Processing 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. |
| title | Zero-Shot Voice Conversion via Content-Aware Timbre Ensemble and Conditional Flow Matching |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2411.02026 |