Speech Utterance Classification

C. Chelba, Milind Mahajan, and Alex Acero

Abstract

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.

Details

Publication typeInproceedings
Published inProc. of the Int. Conf. on Acoustics, Speech, and Signal Processing
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