Discriminative training of n-gram classifiers for speech and text routing
نویسندگان
چکیده
We present a method for conditional maximum likelihood estimation of N-gram models used for text or speech utterance classification. The method employs a well known technique relying on a generalization of the Baum-Eagon inequality from polynomials to rational functions. The best performance is achieved for the 1-gram classifier where conditional maximum likelihood training reduces the class error rate over a maximum likelihood classifier by 45% relative.
منابع مشابه
Speech utterance classification
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 classifi...
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