On the choice of classes in MCE based discriminative HMM-training for speech recognizers used in the telephone environment

One of the most commonly used discriminative approaches in parameter estimation for Hidden Markov Models is the Minimum Classification Error (MCE) method ([1]). This paper studies possible choices for the classes (i.e. basic speech units) in MCE training and their application for several tasks suitable for speech driven dialog systems in the telephone environment. The considered choices of classes are HMM states, phonemes, words and sequences of words. The theoretical suitability and practical considerations for the different criteria are discussed. Using the different training criteria consistent experimental results are given for four tasks: non–task–specific training, training for small vocabulary isolated word recognition, training for connected digit recognition and for letter recognition. In all experiments not only the objective of the optimization but also the resulting word recognition performance is investigated. It shows that for the given setup only word and word string based criteria are capable to reduce the word error rate.