Neural grapheme to phoneme method improves reading in unsegmented languages
Dictionary-Constrained Grapheme-to-Phoneme for Unsegmented Languages from LLM-Annotated Data
Computation and Language
Summary
Languages like Japanese don’t clearly separate words in text, making it hard for computers to convert written characters into their correct sounds. The authors created a smart computer method that uses dictionary guesses and scores to help figure out pronunciations accurately. They also used large language AI to create millions of example sentences, which helped train their system better. Their new method works much better than older approaches, getting almost perfect reading accuracy on a common Japanese benchmark.
What this means in practice
- •For text-to-speech engineers: Generate more accurate pronunciations in speech systems for unsegmented languages like Japanese using this dictionary-constrained neural method.
- •For speech recognition developers: Improve automatic speech recognition accuracy by employing this context-aware grapheme-to-phoneme system when processing unsegmented language text.
Authors
Rui Hu, Zhenpeng Zhan, Xiaolong Lin
Abstract
Grapheme-to-phoneme (G2P) conversion turns raw text into its phonemic form and is an essential part of both text-to-speech (TTS) and automatic speech recognition (ASR) systems. It is required to be fast, stable and context-aware. For unsegmented languages such as Japanese, G2P additionally couples word segmentation with highly context-dependent polyphone disambiguation, and the scarcity of accurately annotated data remains a bottleneck. In this paper, we present a context-aware neural G2P method that scores paths of a discriminative conditional random field (CRF) over a word lattice constructed from dictionaries. To tackle data scarcity, we utilize large language models (LLMs) to generate more than 2 million sentences. Experimental results demonstrate that our method strongly outperforms conventional morphological analyzer-based methods and neural sequence models. On the Joyo-Kanji-Yomi benchmark, our method reaches 99.62% target word reading accuracy, 0.32% target word phoneme error rate (PER) and 0.14% sentence PER.