Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings
This addresses the bottleneck of acquiring annotated pronunciation dictionaries for speech processing applications, but it is incremental as it builds on existing G2P methods.
The paper tackled the problem of grapheme-to-phoneme conversion by learning pronunciations from speech recordings to reduce reliance on costly manual annotations, resulting in consistent improvements in phone error rate across languages and data amounts.
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.