Differentiable Duration Modeling for End-to-End Text-to-Speech

• URL: http://arxiv.org/abs/2203.11049v1
• Date: Mon, 21 Mar 2022 15:14:44 GMT
• Title: Differentiable Duration Modeling for End-to-End Text-to-Speech
• Authors: Bac Nguyen, Fabien Cardinaux, Stefan Uhlich
• Abstract summary: parallel text-to-speech (TTS) models have recently enabled fast and highly-natural speech synthesis. We propose a differentiable duration method for learning monotonic sequences between input and output. Our model learns to perform high-fidelity synthesis through a combination of adversarial training and matching the total ground-truth duration.
• Score: 6.571447892202893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parallel text-to-speech (TTS) models have recently enabled fast and highly-natural speech synthesis. However, such models 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, a direct text to waveform TTS model is introduced to produce raw audio as output instead of performing neural vocoding. Our model learns to perform 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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