Improving the performance of a Dutch CSR by modeling within-word and cross-word pronunciation variation
This article describes how the performance of a Dutch continuous speech recognizer was improved by modeling pronunciation variation. We propose a general procedure for modeling pronunciation variation. In short, it consists of adding pronunciation variants to the lexicon, retraining phone models and using language models to which the pronunciation variants have been added. First, within-word pronunciation variants were generated by applying a set of five optional phonological rules to the words in the baseline lexicon. Next, a limited number of cross-word processes were modeled, using two different methods. In the first approach, cross-word processes were modeled by directly adding the cross-word variants to the lexicon, and in the second approach this was done by using multi-words. Finally, the combination of the within-word method with the two cross-word methods was tested. The word error rate (WER) measured for the baseline system was 12.75%. Compared to the baseline, a small but statistically significant improvement of 0.68% in WER was measured for the within-word method, whereas both cross-word methods in isolation led to small, non-significant improvements. The combination of the within-word method and cross-word method 2 led to the best result: an absolute improvement of 1.12% in WER was found compared to the baseline, which is a relative improvement of 8.8% in WER.
modeling pronunciation variation continuous speech recognition within-word variation cross-word variation