  Data augmentation via voice conversion (VC) has been successfully applied to
low-resource expressive text-to-speech (TTS) when only neutral data for the
target speaker are available. Although the quality of VC is crucial for this
approach, it is challenging to learn a stable VC model because the amount of
data is limited in low-resource scenarios, and highly expressive speech has
large acoustic variety. To address this issue, we propose a novel data
augmentation method that combines pitch-shifting and VC techniques. Because
pitch-shift data augmentation enables the coverage of a variety of pitch
dynamics, it greatly stabilizes training for both VC and TTS models, even when
only 1,000 utterances of the target speaker's neutral data are available.
Subjective test results showed that a FastSpeech 2-based emotional TTS system
with the proposed method improved naturalness and emotional similarity compared
with conventional methods.
