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arXiv · 2203.11049

AutoTTS: End-to-End Text-to-Speech Synthesis through Differentiable Duration Modeling

Bac Nguyen·Fabien Cardinaux·Stefan Uhlich
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Abstract

Parallel text-to-speech (TTS) models have recently enabled fast and highly-natural speech synthesis. However, they typically require external alignment models, which are not necessarily optimized for the decoder as they are not jointly trained. In this paper, we propose a differentiable duration method for learning monotonic alignments between input and output sequences. Our method is based on a soft-duration mechanism that optimizes a stochastic process in expectation. Using this differentiable duration method, we introduce AutoTTS, a direct text-to-waveform speech synthesis model. AutoTTS enables high-fidelity speech synthesis through a combination of adversarial training and matching the total ground-truth duration. Experimental results show that our model obtains competitive results while enjoying a much simpler training pipeline. Audio samples are available online.

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BibTeXRIS

Bac Nguyen, Fabien Cardinaux, Stefan Uhlich. 2022-03-21. AutoTTS: End-to-End Text-to-Speech Synthesis through Differentiable Duration Modeling. https://arxiv.org/abs/2203.11049

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