Discriminative training of compound-word based multinomial classifiers for speech routing

Abstract

We describe a method for utterance classification for speech routing based on a discriminative training multinomial topic classifier. We propose the utilization of the n-norm a posteriori topic probability of the speech hypothesis as the objective function of a discriminative training step, and explore various ways to compute and maximize this function with respect to model parameters. To avoid obtaining negative probability estimates, we propose an alternative representation of the model parameterization. We utilize our approach in combination with a simple non-discriminative word detection algorithm based on Mutual Information and with a technique to identify the most salient phrases of the domain in order to alleviate the feature sparsity problem. We also explore the post-processing of classification results to further improve the classification performance via a decision tree model and a neural network. Overall, our discriminative trained multinomial system reduces the classification error rate up to 45% in an NLU task of financial transactions comparative to our baseline non-discriminative multinomial system.