  Although end-to-end text-to-speech (TTS) models can generate natural speech,
challenges still remain when it comes to estimating sentence-level phonetic and
prosodic information from raw text in Japanese TTS systems. In this paper, we
propose a method for polyphone disambiguation (PD) and accent prediction (AP).
The proposed method incorporates explicit features extracted from morphological
analysis and implicit features extracted from pre-trained language models
(PLMs). We use BERT and Flair embeddings as implicit features and examine how
to combine them with explicit features. Our objective evaluation results showed
that the proposed method improved the accuracy by 5.7 points in PD and 6.0
points in AP. Moreover, the perceptual listening test results confirmed that a
TTS system employing our proposed model as a front-end achieved a mean opinion
score close to that of synthesized speech with ground-truth pronunciation and
accent in terms of naturalness.

Audio and Speech Processing;Sound