When Humans Growl and Birds Speak: High-Fidelity Voice Conversion from Human to Animal and Designed Sounds
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910975258525696 |
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| author | Kang, Minsu Lee, Seolhee Lee, Choonghyeon Cho, Namhyun |
| author_facet | Kang, Minsu Lee, Seolhee Lee, Choonghyeon Cho, Namhyun |
| contents | Human to non-human voice conversion (H2NH-VC) transforms human speech into animal or designed vocalizations. Unlike prior studies focused on dog-sounds and 16 or 22.05kHz audio transformation, this work addresses a broader range of non-speech sounds, including natural sounds (lion-roars, birdsongs) and designed voice (synthetic growls). To accomodate generation of diverse non-speech sounds and 44.1kHz high-quality audio transformation, we introduce a preprocessing pipeline and an improved CVAE-based H2NH-VC model, both optimized for human and non-human voices. Experimental results showed that the proposed method outperformed baselines in quality, naturalness, and similarity MOS, achieving effective voice conversion across diverse non-human timbres. Demo samples are available at https://nc-ai.github.io/speech/publications/nonhuman-vc/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Humans Growl and Birds Speak: High-Fidelity Voice Conversion from Human to Animal and Designed Sounds Kang, Minsu Lee, Seolhee Lee, Choonghyeon Cho, Namhyun Audio and Speech Processing Artificial Intelligence Machine Learning Sound Signal Processing Human to non-human voice conversion (H2NH-VC) transforms human speech into animal or designed vocalizations. Unlike prior studies focused on dog-sounds and 16 or 22.05kHz audio transformation, this work addresses a broader range of non-speech sounds, including natural sounds (lion-roars, birdsongs) and designed voice (synthetic growls). To accomodate generation of diverse non-speech sounds and 44.1kHz high-quality audio transformation, we introduce a preprocessing pipeline and an improved CVAE-based H2NH-VC model, both optimized for human and non-human voices. Experimental results showed that the proposed method outperformed baselines in quality, naturalness, and similarity MOS, achieving effective voice conversion across diverse non-human timbres. Demo samples are available at https://nc-ai.github.io/speech/publications/nonhuman-vc/ |
| title | When Humans Growl and Birds Speak: High-Fidelity Voice Conversion from Human to Animal and Designed Sounds |
| topic | Audio and Speech Processing Artificial Intelligence Machine Learning Sound Signal Processing |
| url | https://arxiv.org/abs/2505.24336 |