  Several fully end-to-end text-to-speech (TTS) models have been proposed that
have shown better performance compared to cascade models (i.e., training
acoustic and vocoder models separately). However, they often generate unstable
pitch contour with audible artifacts when the dataset contains emotional
attributes, i.e., large diversity of pronunciation and prosody. To address this
problem, we propose Period VITS, a novel end-to-end TTS model that incorporates
an explicit periodicity generator. In the proposed method, we introduce a frame
pitch predictor that predicts prosodic features, such as pitch and voicing
flags, from the input text. From these features, the proposed periodicity
generator produces a sample-level sinusoidal source that enables the waveform
decoder to accurately reproduce the pitch. Finally, the entire model is jointly
optimized in an end-to-end manner with variational inference and adversarial
objectives. As a result, the decoder becomes capable of generating more stable,
expressive, and natural output waveforms. The experimental results showed that
the proposed model significantly outperforms baseline models in terms of
naturalness, with improved pitch stability in the generated samples.
