Abstract
Emotional speech synthesis is a challenging task in speech processing. To build an emotional Text-to-speech (TTS) system, one would need to have a quality emotional dataset of the target speaker. However, collecting such data is difficult, sometimes even impossible. This paper presents our approach that addresses the problem of transplanting a source speaker's emotional expression to a target speaker, one of the Vietnamese Language and Speech Processsing (VLSP) 2022 TTS tasks. Our approach includes a complete data pre-processing pipeline and two training algorithms. We first train a source speaker's expressive TTS model, then adapt the voice characteristics for the target speaker. Empirical results have shown the efficacy of our method in generating the expressive speech of a speaker under a limited training data regime.
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