RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis

This work introduces a predominantly parallel, end-to-end TTS model based on normalizing flows. It extends prior parallel approaches by additionally modeling speech rhythm as a separate generative distribution to facilitate variable token duration during inference. We further propose a robust framework for the on-line extraction of speech-text alignments -- a critical yet highly unstable learning problem in end-to-end TTS frameworks. Our experiments demonstrate that our proposed techniques yield improved alignment quality, better output diversity compared to controlled baselines.

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