# Polyphone disambiguation and accent prediction using pre-trained language models in Japanese TTS front-end

arXiv: https://arxiv.org/abs/2201.09427 · HTML: https://mlsanity.com/paper/2201.09427/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2022.

## Built on by (most cited first, 5 of 5)

- [PauseSpeech: Natural Speech Synthesis via Pre-trained Language Model and Pause-based Prosody Modeling](https://mlsanity.com/paper/2306.07489/) (2023)
- [Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning](https://mlsanity.com/paper/2506.04527/) (2025)
- [Comparative Evaluation of Expressive Japanese Character Text-to-Speech with VITS and Style-BERT-VITS2](https://mlsanity.com/paper/2505.17320/) (2025)
- [Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech](https://mlsanity.com/paper/2206.02147/) (2022)
- [Dictionary-Constrained Grapheme-to-Phoneme for Unsegmented Languages from LLM-Annotated Data](https://mlsanity.com/paper/2609.19805/) (2026)

## Abstract (arXiv)

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.