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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