  This paper proposes a hierarchical and multi-scale variational
autoencoder-based non-autoregressive text-to-speech model (HiMuV-TTS) to
generate natural speech with diverse speaking styles. Recent advances in
non-autoregressive TTS (NAR-TTS) models have significantly improved the
inference speed and robustness of synthesized speech. However, the diversity of
speaking styles and naturalness are needed to be improved. To solve this
problem, we propose the HiMuV-TTS model that first determines the global-scale
prosody and then determines the local-scale prosody via conditioning on the
global-scale prosody and the learned text representation. In addition, we
improve the quality of speech by adopting the adversarial training technique.
Experimental results verify that the proposed HiMuV-TTS model can generate more
diverse and natural speech as compared to TTS models with single-scale
variational autoencoders, and can represent different prosody information in
each scale.
