# AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN

arXiv: https://arxiv.org/abs/2005.05642 · HTML: https://mlsanity.com/paper/2005.05642/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 14 of 14)

- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [AdaSpeech: Adaptive Text to Speech for Custom Voice](https://mlsanity.com/paper/2103.00993/) (2021)
- [Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation](https://mlsanity.com/paper/2106.03153/) (2021)
- [EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model](https://mlsanity.com/paper/2106.09317/) (2021)
- [GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis](https://mlsanity.com/paper/2106.15153/) (2021)
- [Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech](https://mlsanity.com/paper/2106.12896/) (2021)
- [Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation](https://mlsanity.com/paper/2210.15868/) (2022)
- [Adapter-Based Extension of Multi-Speaker Text-to-Speech Model for New Speakers](https://mlsanity.com/paper/2211.00585/) (2022)
- [Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module](https://mlsanity.com/paper/2202.08164/) (2022)
- [Review of end-to-end speech synthesis technology based on deep learning](https://mlsanity.com/paper/2104.09995/) (2021)
- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)
- [AdaVocoder: Adaptive Vocoder for Custom Voice](https://mlsanity.com/paper/2203.09825/) (2022)
- [Semi-Supervised Learning Based on Reference Model for Low-resource TTS](https://mlsanity.com/paper/2210.14723/) (2022)
- [AutoLV: Automatic Lecture Video Generator](https://mlsanity.com/paper/2209.08795/) (2022)

## Abstract (arXiv)

This paper investigates how to leverage a DurIAN-based average model to enable a new speaker to have both accurate pronunciation and fluent cross-lingual speaking with very limited monolingual data. A weakness of the recently proposed end-to-end text-to-speech (TTS) systems is that robust alignment is hard to achieve, which hinders it to scale well with very limited data. To cope with this issue, we introduce AdaDurIAN by training an improved DurIAN-based average model and leverage it to few-shot learning with the shared speaker-independent content encoder across different speakers. Several few-shot learning tasks in our experiments show AdaDurIAN can outperform the baseline end-to-end system by a large margin. Subjective evaluations also show that AdaDurIAN yields higher mean opinion score (MOS) of naturalness and more preferences of speaker similarity. In addition, we also apply AdaDurIAN to emotion transfer tasks and demonstrate its promising performance.