# TechSinger: Technique Controllable Multilingual Singing Voice Synthesis via Flow Matching

arXiv: https://arxiv.org/abs/2502.12572 · HTML: https://mlsanity.com/paper/2502.12572/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2025.

## Built on by (most cited first, 19 of 19)

- [SingMOS-Pro: An Comprehensive Benchmark for Singing Quality Assessment](https://mlsanity.com/paper/2510.01812/) (2025)
- [TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis](https://mlsanity.com/paper/2505.14910/) (2025)
- [Vevo2: A Unified and Controllable Framework for Speech and Singing Voice Generation](https://mlsanity.com/paper/2508.16332/) (2025)
- [Versatile Framework for Song Generation with Prompt-based Control](https://mlsanity.com/paper/2504.19062/) (2025)
- [CoMelSinger: Discrete Token-Based Zero-Shot Singing Synthesis With Structured Melody Control and Guidance](https://mlsanity.com/paper/2509.19883/) (2025)
- [DiTSinger: Scaling Singing Voice Synthesis with Diffusion Transformer and Implicit Alignment](https://mlsanity.com/paper/2510.09016/) (2025)
- [Controllable Singing Voice Synthesis using Phoneme-Level Energy Sequence](https://mlsanity.com/paper/2509.07038/) (2025)
- [LTA-L2S: Lexical Tone-Aware Lip-to-Speech Synthesis for Mandarin with Cross-Lingual Transfer Learning](https://mlsanity.com/paper/2509.25670/) (2025)
- [Towards Streaming Synchronized Spatial Audio Generation via Autoregressive Diffusion Transformer](https://mlsanity.com/paper/2605.30940/) (2026)
- [Leveraging Pretrained Diffusion Models for Zero-Shot Part Assembly](https://mlsanity.com/paper/2505.00426/) (2025)
- [Systematizing LLM Persona Design: A Four-Quadrant Technical Taxonomy for AI Companion Applications](https://mlsanity.com/paper/2511.02979/) (2025)
- [Mitigating Latent Mismatch in cVAE-Based Singing Voice Synthesis via Flow Matching](https://mlsanity.com/paper/2601.00217/) (2026)
- [Tutti: Expressive Multi-Singer Synthesis via Structure-Level Timbre Control and Vocal Texture Modeling](https://mlsanity.com/paper/2602.08233/) (2026)
- [Aligning Language Models for Lyric-to-Melody Generation with Rule-Based Musical Constraints](https://mlsanity.com/paper/2604.18489/) (2026)
- [VocalParse: Towards Unified and Scalable Singing Voice Transcription with Large Audio Language Models](https://mlsanity.com/paper/2605.04613/) (2026)
- [Vibrato Expression Control for Singing Voice Conversion with Improving Independent Control](https://mlsanity.com/paper/2606.17126/) (2026)
- [MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation](https://mlsanity.com/paper/2607.26698/) (2026)
- [VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition](https://mlsanity.com/paper/2607.27768/) (2026)
- [VIOLET: High-Fidelity Violin Synthesis with Techniques and Dynamics](https://mlsanity.com/paper/2608.07944/) (2026)

## Abstract (arXiv)

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 at https://gwx314.github.io/tech-singer/.