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The availability of data in expressive styles across languages is limited,and recording sessions are costly and time consuming. To overcome these issues,we demonstrate how to build low-resource, neural text-to-speech (TTS) voiceswith only 1 hour of conversational speech, when no other conversational dataare available in the same language. Assuming the availability of non-expressivespeech data in that language, we propose a 3-step technology: 1) we train anF0-conditioned voice conversion (VC) model as data augmentation technique; 2)we train an F0 predictor to control the conversational flavour of thevoice-converted synthetic data; 3) we train a TTS system that consumes theaugmented data. We prove that our technology enables F0 controllability, isscalable across speakers and languages and is competitive in terms ofnaturalness over a state-of-the-art baseline model, another augmented methodwhich does not make use of F0 information.

Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation