TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909677655162880 |
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| author | Jeong, Jaeseok Lee, Yuna Kwon, Mingi Uh, Youngjung |
| author_facet | Jeong, Jaeseok Lee, Yuna Kwon, Mingi Uh, Youngjung |
| contents | Recent advances in text-to-speech (TTS) have enabled natural speech synthesis, but fine-grained, time-varying emotion control remains challenging. Existing methods often allow only utterance-level control and require full model fine-tuning with a large emotion speech dataset, which can degrade performance. Inspired by adding conditional control to the existing model in ControlNet (Zhang et al, 2023), we propose the first ControlNet-based approach for controllable flow-matching TTS (TTS-CtrlNet), which freezes the original model and introduces a trainable copy of it to process additional conditions. We show that TTS-CtrlNet can boost the pretrained large TTS model by adding intuitive, scalable, and time-varying emotion control while inheriting the ability of the original model (e.g., zero-shot voice cloning & naturalness). Furthermore, we provide practical recipes for adding emotion control: 1) optimal architecture design choice with block analysis, 2) emotion-specific flow step, and 3) flexible control scale.
Experiments show that ours can effectively add an emotion controller to existing TTS, and achieves state-of-the-art performance with emotion similarity scores: Emo-SIM and Aro-Val SIM. The project page is available at: https://curryjung.github.io/ttsctrlnet_project_page |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04349 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet Jeong, Jaeseok Lee, Yuna Kwon, Mingi Uh, Youngjung Sound Audio and Speech Processing Recent advances in text-to-speech (TTS) have enabled natural speech synthesis, but fine-grained, time-varying emotion control remains challenging. Existing methods often allow only utterance-level control and require full model fine-tuning with a large emotion speech dataset, which can degrade performance. Inspired by adding conditional control to the existing model in ControlNet (Zhang et al, 2023), we propose the first ControlNet-based approach for controllable flow-matching TTS (TTS-CtrlNet), which freezes the original model and introduces a trainable copy of it to process additional conditions. We show that TTS-CtrlNet can boost the pretrained large TTS model by adding intuitive, scalable, and time-varying emotion control while inheriting the ability of the original model (e.g., zero-shot voice cloning & naturalness). Furthermore, we provide practical recipes for adding emotion control: 1) optimal architecture design choice with block analysis, 2) emotion-specific flow step, and 3) flexible control scale. Experiments show that ours can effectively add an emotion controller to existing TTS, and achieves state-of-the-art performance with emotion similarity scores: Emo-SIM and Aro-Val SIM. The project page is available at: https://curryjung.github.io/ttsctrlnet_project_page |
| title | TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.04349 |