SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914658386968576 |
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| author | Li, Junjie Guo, Yiwei Chen, Xie Yu, Kai |
| author_facet | Li, Junjie Guo, Yiwei Chen, Xie Yu, Kai |
| contents | Zero-shot voice conversion (VC) aims to transfer the source speaker timbre to arbitrary unseen target speaker timbre, while keeping the linguistic content unchanged. Although the voice of generated speech can be controlled by providing the speaker embedding of the target speaker, the speaker similarity still lags behind the ground truth recordings. In this paper, we propose SEF-VC, a speaker embedding free voice conversion model, which is designed to learn and incorporate speaker timbre from reference speech via a powerful position-agnostic cross-attention mechanism, and then reconstruct waveform from HuBERT semantic tokens in a non-autoregressive manner. The concise design of SEF-VC enhances its training stability and voice conversion performance. Objective and subjective evaluations demonstrate the superiority of SEF-VC to generate high-quality speech with better similarity to target reference than strong zero-shot VC baselines, even for very short reference speeches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_08676 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention Li, Junjie Guo, Yiwei Chen, Xie Yu, Kai Sound Computation and Language Audio and Speech Processing Zero-shot voice conversion (VC) aims to transfer the source speaker timbre to arbitrary unseen target speaker timbre, while keeping the linguistic content unchanged. Although the voice of generated speech can be controlled by providing the speaker embedding of the target speaker, the speaker similarity still lags behind the ground truth recordings. In this paper, we propose SEF-VC, a speaker embedding free voice conversion model, which is designed to learn and incorporate speaker timbre from reference speech via a powerful position-agnostic cross-attention mechanism, and then reconstruct waveform from HuBERT semantic tokens in a non-autoregressive manner. The concise design of SEF-VC enhances its training stability and voice conversion performance. Objective and subjective evaluations demonstrate the superiority of SEF-VC to generate high-quality speech with better similarity to target reference than strong zero-shot VC baselines, even for very short reference speeches. |
| title | SEF-VC: Speaker Embedding Free Zero-Shot Voice Conversion with Cross Attention |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2312.08676 |