Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Low-data? No problem: low-resource, language-agnostic conversational
text-to-speech via F0-conditioned data augmentation
summary: 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) voices
with only 1 hour of conversational speech, when no other conversational data
are available in the same language. Assuming the availability of non-expressive
speech data in that language, we propose a 3-step technology: 1) we train an
F0-conditioned voice conversion (VC) model as data augmentation technique; 2)
we train an F0 predictor to control the conversational flavour of the
voice-converted synthetic data; 3) we train a TTS system that consumes the
augmented data. We prove that our technology enables F0 controllability, is
scalable across speakers and languages and is competitive in terms of
naturalness over a state-of-the-art baseline model, another augmented method
which does not make use of F0 information.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation
summary: 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) voices with only 1 hour of conversational speech, when no other conversational data are available in the same language. Assuming the availability of non-expressive speech data in that language, we propose a 3-step technology: 1) we train an F0-conditioned voice conversion (VC) model as data augmentation technique; 2) we train an F0 predictor to control the conversational flavour of the voice-converted synthetic data; 3) we train a TTS system that consumes the augmented data. We prove that our technology enables F0 controllability, is scalable across speakers and languages and is competitive in terms of naturalness over a state-of-the-art baseline model, another augmented method which does not make use of F0 information.
id: http://arxiv.org/abs/2207.14607v1
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