Corrective Models for Speech Recognition of Inflected Languages

This paper presents a corrective model for speech recognition of inflected languages. The model, based on a discriminative framework, incorporates word ngrams features as well as factored morphological features, providing error reduction over the model based solely on word n-gram features. Experiments on a large vocabulary task, namely the Czech portion of the MALACH corpus, demonstrate performance gain of about 1.1–1.5 % absolute in word error rate, wherein morphological features contribute about a third of the improvement. A simple feature selection mechanism based on χ 2 statistics is shown to be effective in reducing the number of features by about 70 % without any loss in performance, making it feasible to explore yet larger feature spaces.