A minimum classification error based distance measure for template based speech recognition

In this paper we investigate the minimum classification error (MCE) criterion for the training of distance measures for template based speech recognition. These MCE-based distance measures are illustrated with example experiments on the Wall Street Journal 5k benchmark for continuous speech recognition.