NDLI: Speech stuttering assessment using sample entropy and Least Square Support Vector Machine

Content Provider	IEEE Xplore Digital Library
Author	Hariharan, M. Vijean, V. Fook, C.Y. Yaacob, S.
Copyright Year	2012
Description	Author affiliation: School of Mechatronic Engineering, University Malaysia Perlis, Perlis, Malaysia (Hariharan, M.; Vijean, V.; Fook, C.Y.; Yaacob, S.)
Abstract	This work is intended to discuss the performance of sample entropy feature for the recognition of stuttered events. The data for the analysis is taken from the UCLASS database. Manual segmentation is performed to identify the stuttered events prior to the feature extraction process. Wavelet packet decomposition is performed, and the sample entropy features are extracted using three different filter banks, Bark scale, Mel scale and Erb scale. The extracted features are tested using Least Square Support Vector Machine (LS SVM) for the identification of repetition and prolongation. Ten fold cross validation method is used to ensure the reliability of the results. The experimental investigations reveal that the proposed method shows promising results in distinguishing between the two stuttering events.
Starting Page	240
Ending Page	245
File Size	843696
Page Count	6
File Format	PDF
ISBN	9781467309608
e-ISBN	9781467309615
DOI	10.1109/CSPA.2012.6194726
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least square support vector machine wavelet packet decomposition sample entropy Entropy Erb Scale Support vector machines Accuracy Mel scale Speech recognition stuttered events Speech Feature extraction Wavelet packets Bark scale
Content Type	Text
Resource Type	Article

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