The Grapheme-to-Phoneme (G2P) task aims to convert orthographic input into a
discrete phonetic representation. G2P conversion is beneficial to various
speech processing applications, such as text-to-speech and speech recognition.
However, these tend to rely on manually-annotated pronunciation dictionaries,
which are often time-consuming and costly to acquire. In this paper, we propose
a method to improve the G2P conversion task by learning pronunciation examples
from audio recordings. Our approach bootstraps a G2P with a small set of
annotated examples. The G2P model is used to train a multilingual phone
recognition system, which then decodes speech recordings with a phonetic
representation. Given hypothesized phoneme labels, we learn pronunciation
dictionaries for out-of-vocabulary words, and we use those to re-train the G2P
system. Results indicate that our approach consistently improves the phone
error rate of G2P systems across languages and amount of available data.

通过从音频记录中学习发音示例来改善 G2P 转换任务的方法，利用少量带有注释的示例训练 G2P 模型，使用多语言电话识别系统进行解码，学习对于未收录词汇的发音字典，并重新训练 G2P 系统，该方法在不同语言和数据量的情况下持续提高 G2P 系统的音素错误率。