Enabling Beam Search for Language Model-Based Text-to-Speech Synthesis
Zehai Tu, Guangyan Zhang, Yiting Lu, Adaeze Adigwe, Simon King, Yiwen Guo
Abstract
Tokenising continuous speech into sequences of discrete tokens and modelling them with language models (LMs) has led to significant success in text-to-speech (TTS) synthesis. Despite these models can generate speech with high quality and naturalness, their synthesised samples can still suffer from artefacts, mispronunciation, word repeating, etc. In this paper, we argue these undesirable properties could partly be caused by the randomness of sampling-based strategies during the autoregressive decoding of LMs. Therefore, we look at maximization-based decoding approaches and propose Temporal Repetition Aware Diverse Beam Search (TRAD-BS) to find the most probable sequences of the generated speech tokens. Experiments with two recent LM-based TTS models demonstrate that our proposed maximisation-based decoding strategy generates speech with fewer mispronunciations and improved speaker consistency<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.
BibTeX
@inproceedings{icassp2025_enablingbeamsear,
title = {Enabling Beam Search for Language Model-Based Text-to-Speech Synthesis},
author = {Zehai Tu and Guangyan Zhang and Yiting Lu and Adaeze Adigwe and Simon King and Yiwen Guo},
booktitle = {ICASSP 2025},
year = {2025}
}