The paper presents a series of experiments on speech utterance

classification performed on the ATIS corpus. We

compare the performance of n-gram classifiers with that of

Naive Bayes and maximum entropy classifiers. The n-gram

classifiers have the advantage that one can use a single pass

system (concurrent speech recognition and classification)

whereas for Naive Bayes or maximum entropy classification

we use a two-stage system: speech recognition followed

by classification. Substantial relative improvements

(up to 55%) in classification accuracy can be obtained using

discriminative training methods that belong to the class of

conditional maximum likelihood techniques.

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In  Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing

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