  Recently, denoising diffusion probabilistic models and generative score
matching have shown high potential in modelling complex data distributions
while stochastic calculus has provided a unified point of view on these
techniques allowing for flexible inference schemes. In this paper we introduce
Grad-TTS, a novel text-to-speech model with score-based decoder producing
mel-spectrograms by gradually transforming noise predicted by encoder and
aligned with text input by means of Monotonic Alignment Search. The framework
of stochastic differential equations helps us to generalize conventional
diffusion probabilistic models to the case of reconstructing data from noise
with different parameters and allows to make this reconstruction flexible by
explicitly controlling trade-off between sound quality and inference speed.
Subjective human evaluation shows that Grad-TTS is competitive with
state-of-the-art text-to-speech approaches in terms of Mean Opinion Score. We
will make the code publicly available shortly.
