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.