Stepback: Enhanced Disentanglement for Voice Conversion via Multi-Task Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915123143114752 |
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| author | Yang, Qian Graham, Calbert |
| author_facet | Yang, Qian Graham, Calbert |
| contents | Voice conversion (VC) modifies voice characteristics while preserving linguistic content. This paper presents the Stepback network, a novel model for converting speaker identity using non-parallel data. Unlike traditional VC methods that rely on parallel data, our approach leverages deep learning techniques to enhance disentanglement completion and linguistic content preservation. The Stepback network incorporates a dual flow of different domain data inputs and uses constraints with self-destructive amendments to optimize the content encoder. Extensive experiments show that our model significantly improves VC performance, reducing training costs while achieving high-quality voice conversion. The Stepback network's design offers a promising solution for advanced voice conversion tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15613 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stepback: Enhanced Disentanglement for Voice Conversion via Multi-Task Learning Yang, Qian Graham, Calbert Sound Computation and Language Audio and Speech Processing Voice conversion (VC) modifies voice characteristics while preserving linguistic content. This paper presents the Stepback network, a novel model for converting speaker identity using non-parallel data. Unlike traditional VC methods that rely on parallel data, our approach leverages deep learning techniques to enhance disentanglement completion and linguistic content preservation. The Stepback network incorporates a dual flow of different domain data inputs and uses constraints with self-destructive amendments to optimize the content encoder. Extensive experiments show that our model significantly improves VC performance, reducing training costs while achieving high-quality voice conversion. The Stepback network's design offers a promising solution for advanced voice conversion tasks. |
| title | Stepback: Enhanced Disentanglement for Voice Conversion via Multi-Task Learning |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.15613 |