2026-03-06T09:00:59Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00090264 2025-01-20T06:49:17Z 581:7002:7080

Comparison of Methods for Topic Classification of Spoken Inquiries Comparison of Methods for Topic Classification of Spoken Inquiries Rafael, Torres Hiromichi, Kawanami Tomoko, Matsui Hiroshi, Saruwatari Kiyohiro, Shikano Rafael, Torres Hiromichi, Kawanami Tomoko, Matsui Hiroshi, Saruwatari Kiyohiro, Shikano [特集：音声ドキュメント処理] topic classification, support vector machine, PrefixSpan boosting, maximum entropy, stacked generalization In this work, we address the topic classification of spoken inquiries in Japanese that are received by a speech-oriented guidance system operating in a real environment. The classification of spoken inquiries is often hindered by automatic speech recognition (ASR) errors, the sparseness of features and the shortness of spontaneous speech utterances. Here, we compare the performances of a support vector machine (SVM) with a radial basis function (RBF) kernel, PrefixSpan boosting (pboost) and the maximum entropy (ME) method, which are supervised learning methods. We also combine their predictions using a stacked generalization (SG) scheme. We also perform an evaluation using words or characters as features for the classifiers. Using characters as features is possible in Japanese owing to the presence of kanji, ideograms originating from Chinese characters that represent not only sounds but also meanings. We performed analyses on the performance of the above methods and their combination in dealing with the indicated problems. Experimental results show an F-measure of 86.87% for the classification of ASR results from children's inquiries with an average performance improvement of 2.81% compared with the performance of individual classifiers, and an F-measure of 93.96% with an average improvement of 1.89% for adults' inquiries when using the SG scheme and character features. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.21(2013) No.2 (online) DOI http://dx.doi.org/10.2197/ipsjjip.21.157 ------------------------------ In this work, we address the topic classification of spoken inquiries in Japanese that are received by a speech-oriented guidance system operating in a real environment. The classification of spoken inquiries is often hindered by automatic speech recognition (ASR) errors, the sparseness of features and the shortness of spontaneous speech utterances. Here, we compare the performances of a support vector machine (SVM) with a radial basis function (RBF) kernel, PrefixSpan boosting (pboost) and the maximum entropy (ME) method, which are supervised learning methods. We also combine their predictions using a stacked generalization (SG) scheme. We also perform an evaluation using words or characters as features for the classifiers. Using characters as features is possible in Japanese owing to the presence of kanji, ideograms originating from Chinese characters that represent not only sounds but also meanings. We performed analyses on the performance of the above methods and their combination in dealing with the indicated problems. Experimental results show an F-measure of 86.87% for the classification of ASR results from children's inquiries with an average performance improvement of 2.81% compared with the performance of individual classifiers, and an F-measure of 93.96% with an average improvement of 1.89% for adults' inquiries when using the SG scheme and character features. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.21(2013) No.2 (online) DOI http://dx.doi.org/10.2197/ipsjjip.21.157 ------------------------------ 2013-02-15 eng journal article https://ipsj.ixsq.nii.ac.jp/records/90264 1882-7764 AN00116647 情報処理学会論文誌 54 2 https://ipsj.ixsq.nii.ac.jp/record/90264/files/IPSJ-JNL5402004.pdf application/pdf 1.5 MB 2015-02-15