# TDASS: Target Domain Adaptation Speech Synthesis Framework for Multi-speaker Low-Resource TTS

arXiv: https://arxiv.org/abs/2205.11824 · HTML: https://mlsanity.com/paper/2205.11824/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2022.

## Built on by (most cited first, 5 of 5)

- [ADAPTERMIX: Exploring the Efficacy of Mixture of Adapters for Low-Resource TTS Adaptation](https://mlsanity.com/paper/2305.18028/) (2023)
- [Pre-Avatar: An Automatic Presentation Generation Framework Leveraging Talking Avatar](https://mlsanity.com/paper/2210.06877/) (2022)
- [Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach](https://mlsanity.com/paper/2210.13805/) (2022)
- [Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data](https://mlsanity.com/paper/2210.13803/) (2022)
- [EM-TTS: Efficiently Trained Low-Resource Mongolian Lightweight Text-to-Speech](https://mlsanity.com/paper/2403.08164/) (2024)

## Abstract (arXiv)

Recently, synthesizing personalized speech by text-to-speech (TTS) application is highly demanded. But the previous TTS models require a mass of target speaker speeches for training. It is a high-cost task, and hard to record lots of utterances from the target speaker. Data augmentation of the speeches is a solution but leads to the low-quality synthesis speech problem. Some multi-speaker TTS models are proposed to address the issue. But the quantity of utterances of each speaker imbalance leads to the voice similarity problem. We propose the Target Domain Adaptation Speech Synthesis Network (TDASS) to address these issues. Based on the backbone of the Tacotron2 model, which is the high-quality TTS model, TDASS introduces a self-interested classifier for reducing the non-target influence. Besides, a special gradient reversal layer with different operations for target and non-target is added to the classifier. We evaluate the model on a Chinese speech corpus, the experiments show the proposed method outperforms the baseline method in terms of voice quality and voice similarity.