Improving the Performance of a Dutch CSR by Modeling Pronunciation Variation

Abstract

This paper describes how the performance of a continuous speech recognizer for Dutch has been improved by modeling pronunciation variation. We used three methods in order to model pronunciation variation. First, within-word variation was dealt with. Phonological rules were applied to the words in the lexicon, thus automatically generating pronunciation variants. Secondly, cross-word pronunciation variation was accounted for by adding multi-words and their variants to the lexicon. Thirdly, probabilities of pronunciation variants were incorporated in the language model (LM), and thresholds were used to choose which pronunciation variants to add to the LMs. For each of the methods, recognition experiments were carried out. A significant improvement in error rates was measured