  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.
