Comparison of Methods for Topic Classification of Spoken Inquiries
-
- Torres Rafael
- Nara Institute of Science and Technology
-
- Kawanami Hiromichi
- Nara Institute of Science and Technology
-
- Matsui Tomoko
- The Institute of Statistical Mathematics
-
- Saruwatari Hiroshi
- Nara Institute of Science and Technology
-
- Shikano Kiyohiro
- Nara Institute of Science and Technology
Search this article
Abstract
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.
Journal
-
- Journal of Information Processing
-
Journal of Information Processing 21 (2), 157-167, 2013
Information Processing Society of Japan
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390282680272704256
-
- NII Article ID
- 130003369518
-
- NII Book ID
- AA00700121
-
- ISSN
- 18826652
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- IRDB
- Crossref
- CiNii Articles
- KAKEN
-
- Abstract License Flag
- Disallowed