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Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy

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Main Authors: Hariharan, Muthusamy, Dr., Chong, Yen Fook, Sindhu, Ravindran, Abdul Hamid, Adom, Prof. Dr., Sazali, Yaacob, Prof. Dr.
Other Authors: hari@unimap.edu.my
Format: Article
Language:English
Published: Elsevier Inc 2014
Subjects:
Dysfluent speech
kNN
LDA and SVM
Sample entropy
Wavelet packet transform
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/33147
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spelling my.unimap-331472014-03-27T06:38:13Z Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy Hariharan, Muthusamy, Dr. Chong, Yen Fook Sindhu, Ravindran Abdul Hamid, Adom, Prof. Dr. Sazali, Yaacob, Prof. Dr. hari@unimap.edu.my fook1987@gmail.com abdhamid@unimap.edu.my s.yaacob@unimap.edu.my Dysfluent speech kNN LDA and SVM Sample entropy Wavelet packet transform Link to publisher's homepage at https://www.elsevier.com/ Dysfluency and stuttering are a break or interruption of normal speech such as repetition, prolongation, interjection of syllables, sounds, words or phrases and involuntary silent pauses or blocks in communication. Stuttering assessment through manual classification of speech dysfluencies is subjective, inconsistent, time consuming and prone to error. This paper proposes an objective evaluation of speech dysfluencies based on the wavelet packet transform with sample entropy features. Dysfluent speech signals are decomposed into six levels by using wavelet packet transform. Sample entropy (SampEn) features are extracted at every level of decomposition and they are used as features to characterize the speech dysfluencies (stuttered events). Three different classifiers such as k-nearest neighbor (kNN), linear discriminant analysis (LDA) based classifier and support vector machine (SVM) are used to investigate the performance of the sample entropy features for the classification of speech dysfluencies. 10-fold cross validation method is used for testing the reliability of the classifier results. The effect of different wavelet families on the classification performance is also performed. Experimental results demonstrate that the proposed features and classification algorithms give very promising classification accuracy of 96.67% with the standard deviation of 0.37 and also that the proposed method can be used to help speech language pathologist in classifying speech dysfluencies. 2014-03-26T07:57:52Z 2014-03-26T07:57:52Z 2013-05 Article Digital Signal Processing: A Review Journal, vol. 23(3), 2013, pages 952-959 1051-2004 http://www.sciencedirect.com/science/article/pii/S1051200412003016?via=ihub http://dspace.unimap.edu.my:80/dspace/handle/123456789/33147 en Elsevier Inc
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Dysfluent speech
kNN
LDA and SVM
Sample entropy
Wavelet packet transform
spellingShingle Dysfluent speech
kNN
LDA and SVM
Sample entropy
Wavelet packet transform
Hariharan, Muthusamy, Dr.
Chong, Yen Fook
Sindhu, Ravindran
Abdul Hamid, Adom, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
description Link to publisher's homepage at https://www.elsevier.com/
author2 hari@unimap.edu.my
author_facet hari@unimap.edu.my
Hariharan, Muthusamy, Dr.
Chong, Yen Fook
Sindhu, Ravindran
Abdul Hamid, Adom, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
format Article
author Hariharan, Muthusamy, Dr.
Chong, Yen Fook
Sindhu, Ravindran
Abdul Hamid, Adom, Prof. Dr.
Sazali, Yaacob, Prof. Dr.
author_sort Hariharan, Muthusamy, Dr.
title Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
title_short Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
title_full Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
title_fullStr Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
title_full_unstemmed Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
title_sort objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy
publisher Elsevier Inc
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/33147
_version_ 1643797082112262144
score 13.222552

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