Voice Conversion for Likability Control via Automated Rating of Speech Synthesis Corpora
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908430092992512 |
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| author | Suda, Hitoshi Takamichi, Shinnosuke Fukayama, Satoru |
| author_facet | Suda, Hitoshi Takamichi, Shinnosuke Fukayama, Satoru |
| contents | Perceived voice likability plays a crucial role in various social interactions, such as partner selection and advertising. A system that provides reference likable voice samples tailored to target audiences would enable users to adjust their speaking style and voice quality, facilitating smoother communication. To this end, we propose a voice conversion method that controls the likability of input speech while preserving both speaker identity and linguistic content. To improve training data scalability, we train a likability predictor on an existing voice likability dataset and employ it to automatically annotate a large speech synthesis corpus with likability ratings. Experimental evaluations reveal a significant correlation between the predictor's outputs and human-provided likability ratings. Subjective and objective evaluations further demonstrate that the proposed approach effectively controls voice likability while preserving both speaker identity and linguistic content. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01356 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Voice Conversion for Likability Control via Automated Rating of Speech Synthesis Corpora Suda, Hitoshi Takamichi, Shinnosuke Fukayama, Satoru Audio and Speech Processing Sound Perceived voice likability plays a crucial role in various social interactions, such as partner selection and advertising. A system that provides reference likable voice samples tailored to target audiences would enable users to adjust their speaking style and voice quality, facilitating smoother communication. To this end, we propose a voice conversion method that controls the likability of input speech while preserving both speaker identity and linguistic content. To improve training data scalability, we train a likability predictor on an existing voice likability dataset and employ it to automatically annotate a large speech synthesis corpus with likability ratings. Experimental evaluations reveal a significant correlation between the predictor's outputs and human-provided likability ratings. Subjective and objective evaluations further demonstrate that the proposed approach effectively controls voice likability while preserving both speaker identity and linguistic content. |
| title | Voice Conversion for Likability Control via Automated Rating of Speech Synthesis Corpora |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2507.01356 |