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The Grapheme-to-Phoneme (G2P) task aims to convert orthographic input into adiscrete phonetic representation. G2P conversion is beneficial to variousspeech 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 proposea method to improve the G2P conversion task by learning pronunciation examplesfrom audio recordings. Our approach bootstraps a G2P with a small set ofannotated examples. The G2P model is used to train a multilingual phonerecognition system, which then decodes speech recordings with a phoneticrepresentation. Given hypothesized phoneme labels, we learn pronunciationdictionaries for out-of-vocabulary words, and we use those to re-train the G2Psystem. Results indicate that our approach consistently improves the phoneerror rate of G2P systems across languages and amount of available data.

Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings