Combining classifiers for spoken language understanding

We are interested in the problem of understanding spontaneous speech in the context of human-machine dialogs. Utterance classification is a key component of the understanding process to determine the intent of the user. The paper presents methods for combining different statistical classifiers for spoken language understanding. We propose three combination methods. The first one combines the scores assigned to the call-types by individual classifiers using a voting mechanism. The second method is a cascaded approach. The third method employs a top level learner to decide on the final call-type. We have evaluated these combination methods over three large spoken dialog databases (/spl sim/10/sup 6/ dialogs) collected using the AT&T natural spoken dialog system for customer care applications. The results indicate that it is possible to reduce significantly the error rate of the understanding module using these combination methods.