An application of minimum classification error to feature space transformations for speech recognition

The use of signal transformations is a necessary step for feature extraction in pattern recognition systems. These transformations should take into account the main goal of pattern recognition: the error-rate minimization. In this paper we propose a new method to obtain feature space transformations based on the Minimum Classification Error criterion. The goal of these transformations is to obtain a new representation space where the Euclidean distance is optimal for classification. The proposed method is tested on a speech recognition system using different types of Hidden Markov Models. The comparison with standard pre-processing techniques shows that our method provides an error-rate reduction in all the performed experiments.