NDLI: Compact decision trees with cluster validity for speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Chien, Jen-Tzung Huang, Chih-Hsien Chen, Shun-Ju
Copyright Year	2002
Description	Author affiliation: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan 70101, ROC (Chien, Jen-Tzung; Huang, Chih-Hsien; Chen, Shun-Ju)
Abstract	A decision tree is built by successively splitting the observation frames of a phonetic unit according to the best phonetic questions. To prevent over-large tree models, the stopping criterion is required to suppress tree growing. It is crucial to exploit the goodness-of-split criteria to choose the best questions for node splitting and test if the hypothesis of splitting should be terminated. The robust tree models could be established. In this study, we apply the Hubert's Γ statistic as the node splitting criterion and the $T^{2}-statistic$ as the stopping criterion. Hubert's Γ statistic is a cluster validity measure, which characterizes the degree of clustering in the available data. This measure is useful to select the best questions to unravel tree nodes. Further, we examine the population closeness of two child nodes with a significant level, $T^{2}-statistic$ is determined to validate whether the corresponding mean vectors are close together. The splitting is stopped when validated. In continuous speech recognition experiments, the proposed methods achieve better recognition rates with smaller tree models compared to the maximum likelihood and minimum description length criteria.
File Size	2362809
File Format	PDF
ISBN	0780374029
ISSN	15206149
DOI	10.1109/ICASSP.2002.5743878
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Robustness Decision trees Covariance matrix Data models Benchmark testing Clustering algorithms
Content Type	Text
Resource Type	Article

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