On string level MCE training in MLP/HMM speech recognition system

This paper considers both the garbage modelling and the discriminative training of a speech recognition system consisting of a multilayer perceptron (MLP) network and hidden Markov models (HMMs). The string level training of the system is based on Minimum Classification Error (MCE) algorithm using especially the ideas recently proposed by Reichl et al. In order to improve the MCE training scheme, they defined a derivative for MCE cost function putting heavier weight to the misclassified utterances than the traditional translated sigmoidal. In this paper, we give a definition of a new cost function, whose derivative has similar properties as the one proposed by Reichl et al. The new and the sigmoidal cost function are compared with the string level (MCE) algorithm. The string recognition rates show that both methods achieve equal performances, but the convergence of the MCE algorithm is a bit faster with the new cost function. Moreover, the performance of two garbage models, the nth best and an interpolative garbage model, are also compared in this paper. The latter one is an approximation of the former one requiring less operations, but achieving comparable performance. The recognition system achieved 93.32% accuracy for test set at best. The test set contained 29188 Finnish digit strings from two environments