Comparison of methods for topic classification in a speech-oriented guidance system

This work addresses the classification in topics of utterances in Japanese, received by a speech-oriented guidance system operating in a real environment. For this, we compare the performance of Support Vector Machine and PrefixSpan Boosting, against a conventional Maximum Entropy classification method. We are interested in evaluating their strength against automatic speech recognition (ASR) errors and the sparseness of the features present in spontaneous speech. To deal with the shortness of the utterances, we also proposed to use characters as features instead of words, which is possible with the Japanese language due to the presence of kanji; ideograms from Chinese characters that represent not only sound but meaning. Experimental results show a classification performance improvement from 92.2% to 94.4%, with Support Vector Machine using character unigrams and bigrams as features, in comparison to the conventional method.