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 n-grams 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 X2 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.