Stacked generalization is a method that allows combining output of multiple classifiers using a second-level classifi- cation, minimizing the generalization error of first-level classifiers and achieving greater predictive accuracy. In a previous work, we compared the performance of support vector machine (SVM) with radial basis function (RBF) kernel, prefixspan boosting (pboost) and maximum entropy (ME) in the classification in topics of spoken inquiries in Japanese received by a guidance system. In the present work, we employ a stacked generalization scheme that uses predictions of SVM with RBF kernel, pboost and ME as input for a second-level classification using linear SVM. Experimental results show an improvement in performance from 94.2% to 95.1% in the classification of automatic speech recognition (ASR) 1-best results of adults’ inquiries and from 88.3% to 89.2% for children’s inquiries, when using stacked generalization in comparison to the individual performance of the first-level classifiers.
展开▼
