In this paper a previously proposed method for the automatic construction of a lexicon with pronunciation variants for ASR is further developed and evaluated. The basic idea is to transform a lexicon of canonical forms by means of rewrite rules that are learned automatically on a training corpus of orthographically transcribed utterances. The method is evaluated on the TIMIT corpus, using a speech recognizer incorporating context-independent HMMs and a bigram language model. It appears that reductions of the word error rate of up to 35 % are possible to achieve. However, it also appears that it is more likely to obtain much lower gains.
Cite as: Yang, Q., Martens, J.-P., Ghesquiere, P.-J., Compernolle, D.V. (2002) Pronunciation variation modeling for asr: large improvements are possible but small ones are likely. Proc. ITRW on Pronunciation Modeling and Lexicon Adaptation for Spoken Language Technology (PMLA 2002), 123-128
@inproceedings{yang02_pmla,
author={Qian Yang and Jean-Pierre Martens and Pieter-Jan Ghesquiere and Dirk Van Compernolle},
title={{Pronunciation variation modeling for asr: large improvements are possible but small ones are likely}},
year=2002,
booktitle={Proc. ITRW on Pronunciation Modeling and Lexicon Adaptation for Spoken Language Technology (PMLA 2002)},
pages={123--128}
}