# Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis

arXiv: https://arxiv.org/abs/1904.02373 · HTML: https://mlsanity.com/paper/1904.02373/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 11 of 11)

- [V2C: Visual Voice Cloning](https://mlsanity.com/paper/2111.12890/) (2021)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [Cross-speaker style transfer for text-to-speech using data augmentation](https://mlsanity.com/paper/2202.05083/) (2022)
- [QI-TTS: Questioning Intonation Control for Emotional Speech Synthesis](https://mlsanity.com/paper/2303.07682/) (2023)
- [Style-Label-Free: Cross-Speaker Style Transfer by Quantized VAE and Speaker-wise Normalization in Speech Synthesis](https://mlsanity.com/paper/2212.06397/) (2022)
- [Text-driven Emotional Style Control and Cross-speaker Style Transfer in Neural TTS](https://mlsanity.com/paper/2207.06000/) (2022)
- [Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning](https://mlsanity.com/paper/2111.07218/) (2021)
- [Prosody Analysis of Audiobooks](https://mlsanity.com/paper/2310.06930/) (2023)
- [DiCLET-TTS: Diffusion Model based Cross-lingual Emotion Transfer for Text-to-Speech -- A Study between English and Mandarin](https://mlsanity.com/paper/2309.00883/) (2023)
- [Voice Conversion for Lombard Speaking Style with Implicit and Explicit Acoustic Feature Conditioning](https://mlsanity.com/paper/2507.09310/) (2025)
- [Towards Cross-speaker Reading Style Transfer on Audiobook Dataset](https://mlsanity.com/paper/2208.05359/) (2022)

## Abstract (arXiv)

Speech style control and transfer techniques aim to enrich the diversity and expressiveness of synthesized speech. Existing approaches model all speech styles into one representation, lacking the ability to control a specific speech feature independently. To address this issue, we introduce a novel multi-reference structure to Tacotron and propose intercross training approach, which together ensure that each sub-encoder of the multi-reference encoder independently disentangles and controls a specific style. Experimental results show that our model is able to control and transfer desired speech styles individually.