TechSinger: Technique Controllable Multilingual Singing Voice Synthesis via Flow Matching

Singing voice synthesis has made remarkable progress in generating natural and high-quality voices. However, existing methods rarely provide precise control over vocal techniques such as intensity, mixed voice, falsetto, bubble, and breathy tones, thus limiting the expressive potential of synthetic voices. We introduce TechSinger, an advanced system for controllable singing voice synthesis that supports five languages and seven vocal techniques. TechSinger leverages a flow-matching-based generative model to produce singing voices with enhanced expressive control over various techniques. To enhance the diversity of training data, we develop a technique detection model that automatically annotates datasets with phoneme-level technique labels. Additionally, our prompt-based technique prediction model enables users to specify desired vocal attributes through natural language, offering fine-grained control over the synthesized singing. Experimental results demonstrate that TechSinger significantly enhances the expressiveness and realism of synthetic singing voices, outperforming existing methods in terms of audio quality and technique-specific control. Audio samples can be found atthis https URL.

@article{guo2025_2502.12572,
  title={ TechSinger: Technique Controllable Multilingual Singing Voice Synthesis via Flow Matching },
  author={ Wenxiang Guo and Yu Zhang and Changhao Pan and Rongjie Huang and Li Tang and Ruiqi Li and Zhiqing Hong and Yongqi Wang and Zhou Zhao },
  journal={arXiv preprint arXiv:2502.12572},
  year={ 2025 }
}