# Cross-speaker style transfer for text-to-speech using data augmentation

arXiv: https://arxiv.org/abs/2202.05083 · HTML: https://mlsanity.com/paper/2202.05083/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss](https://mlsanity.com/paper/1905.05879/) (2019)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron](https://mlsanity.com/paper/1803.09047/) (2018)
- [Learning latent representations for style control and transfer in end-to-end speech synthesis](https://mlsanity.com/paper/1812.04342/) (2018)
- [Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens](https://mlsanity.com/paper/1910.11997/) (2019)
- [F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder](https://mlsanity.com/paper/2004.07370/) (2020)
- [Low-resource expressive text-to-speech using data augmentation](https://mlsanity.com/paper/2011.05707/) (2020)
- [Universal Neural Vocoding with Parallel WaveNet](https://mlsanity.com/paper/2102.01106/) (2021)
- [CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech](https://mlsanity.com/paper/2004.14617/) (2020)
- [STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech](https://mlsanity.com/paper/2103.09474/) (2021)
- [TTS-by-TTS: TTS-driven Data Augmentation for Fast and High-Quality Speech Synthesis](https://mlsanity.com/paper/2010.13421/) (2020)
- [Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech](https://mlsanity.com/paper/2106.12896/) (2021)
- [Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency](https://mlsanity.com/paper/1910.11958/) (2019)
- [Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis](https://mlsanity.com/paper/1904.02373/) (2019)
- [Cross-speaker Style Transfer with Prosody Bottleneck in Neural Speech Synthesis](https://mlsanity.com/paper/2107.12562/) (2021)
- [Improving Performance of Seen and Unseen Speech Style Transfer in End-to-end Neural TTS](https://mlsanity.com/paper/2106.10003/) (2021)

## Built on by (most cited first, 9 of 9)

- [PromptVC: Flexible Stylistic Voice Conversion in Latent Space Driven by Natural Language Prompts](https://mlsanity.com/paper/2309.09262/) (2023)
- [Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing](https://mlsanity.com/paper/2302.10536/) (2023)
- [Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation](https://mlsanity.com/paper/2204.10020/) (2022)
- [Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech](https://mlsanity.com/paper/2306.05709/) (2023)
- [Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder](https://mlsanity.com/paper/2211.03316/) (2022)
- [Rapid Speaker Adaptation in Low Resource Text to Speech Systems using Synthetic Data and Transfer learning](https://mlsanity.com/paper/2312.01107/) (2023)
- [Voice Conversion for Lombard Speaking Style with Implicit and Explicit Acoustic Feature Conditioning](https://mlsanity.com/paper/2507.09310/) (2025)
- [Data-augmented cross-lingual synthesis in a teacher-student framework](https://mlsanity.com/paper/2204.00061/) (2022)
- [ZeSTA: Zero-Shot TTS Augmentation with Domain-Conditioned Training for Data-Efficient Personalized Speech Synthesis](https://mlsanity.com/paper/2603.04219/) (2026)

## Abstract (arXiv)

We address the problem of cross-speaker style transfer for text-to-speech (TTS) using data augmentation via voice conversion. We assume to have a corpus of neutral non-expressive data from a target speaker and supporting conversational expressive data from different speakers. Our goal is to build a TTS system that is expressive, while retaining the target speaker's identity. The proposed approach relies on voice conversion to first generate high-quality data from the set of supporting expressive speakers. The voice converted data is then pooled with natural data from the target speaker and used to train a single-speaker multi-style TTS system. We provide evidence that this approach is efficient, flexible, and scalable. The method is evaluated using one or more supporting speakers, as well as a variable amount of supporting data. We further provide evidence that this approach allows some controllability of speaking style, when using multiple supporting speakers. We conclude by scaling our proposed technology to a set of 14 speakers across 7 languages. Results indicate that our technology consistently improves synthetic samples in terms of style similarity, while retaining the target speaker's identity.