Adaptive Duration Model for Text Speech Alignment

Abstract:Speech-to-text alignment is a critical component of neural text to speech (TTS) models. Autoregressive TTS models typically use an attention mechanism to learn these alignments on-line, while non-autoregressive end to end TTS models rely on durations extracted from external sources. In this paper, we propose a novel duration prediction framework that can give promising phoneme-level duration distribution with given text. In our experiments, the proposed duration model has more precise prediction and adaptation ability to conditions, compared to previous baseline models. Specifically, it makes a considerable improvement on phoneme-level alignment accuracy and makes the performance of zero-shot TTS models more robust to the mismatch between prompt audio and input audio.

Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)

arXiv:2507.22612 [cs.SD]

(or arXiv:2507.22612v3 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2507.22612 Focus to learn more arXiv-issued DOI via DataCite

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