Topic Classification of Spoken Inquiries Based on Stacked Generalization
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.
Rafael Torres Hiromichi Kawanami Tomoko Matsui Hiroshi Saruwatari Kiyohiro Shikano
Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan Department of Statistical Modeling, The Institute of Statistical Mathematics, Tokyo, Japan
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-4
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）