VALL-T: Decoder-Only Generative Transducer for Robust and Decoding-Controllable Text-to-Speech
Chenpeng Du, Yiwei Guo, Hankun Wang, Yifan Yang, Zhikang Niu, Shuai Wang, Hui Zhang, Xie Chen
Abstract
Recent TTS models with decoder-only Transformer architecture, such as SPEAR-TTS and VALL-E, achieve impressive naturalness and demonstrate the ability for zero-shot adaptation given a speech prompt. However, such decoder-only TTS models lack monotonic alignment constraints, sometimes leading to hallucination issues such as mispronunciation, word skipping and repeating. To address this limitation, we propose VALL-T, a generative Transducer model that introduces shifting relative position embeddings for input phoneme sequence, explicitly indicating the monotonic generation process while maintaining the architecture of decoder-only Transformer. Consequently, VALL-T retains the capability of prompt-based zero-shot adaptation and demonstrates better robustness against hallucinations with a relative reduction of 28.3% in the word error rate. The audio samples are available at https://cpdu.github.io/vallt.
BibTeX
@inproceedings{icassp2025_valltdecoderonly,
title = {VALL-T: Decoder-Only Generative Transducer for Robust and Decoding-Controllable Text-to-Speech},
author = {Chenpeng Du and Yiwei Guo and Hankun Wang and Yifan Yang and Zhikang Niu and Shuai Wang and Hui Zhang and Xie Chen and Kai Yu},
booktitle = {ICASSP 2025},
year = {2025}
}