C. Chelba, Milind Mahajan, and Alex Acero
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.
|Published in||Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing|