Minimum classification error transformations for improving speech recognition systems

Signal representation is an important aspect to be taken into account for pattern classification. Recently, discriminative training methods have been applied to feature extraction for speech recognition. In this paper, we apply the Minimum Classification Error estimation to train the parameters of a feature extractor. This feature extractor is a linear transformation of the original representation space. The new representation of the speech signal makes easier the recognition task and the performance of the different tested recognizers is improved as the experimental results show.