Learning From Mistakes: Expanding Pronunciation Lexicons Using Word Recognition Errors

We introduce the problem of learning pronunciations of out-of-vocabulary words from word recognition mistakes made by an automatic speech recognition (ASR) system. This question is especially relevant in cases where the ASR engine is a black box - meaning that the only acoustic cues about the speech data come from the word recognition outputs. This paper presents an expectation maximization approach to inferring pronunciations from ASR word recognition hypotheses, which outperforms pronunciation estimates of a state of the art grapheme-to-phoneme system.