Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Intelligent Processing of Stuttered Speech
Journal of Intelligent Information Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Optimal parameters study for sample entropy-based atrial fibrillation organization analysis
Computer Methods and Programs in Biomedicine
Classification of speech dysfluencies with MFCC and LPCC features
Expert Systems with Applications: An International Journal
Optimized orthonormal wavelet filters with improved frequency separation
Digital Signal Processing
Recovery of the optimal approximation from samples in wavelet subspace
Digital Signal Processing
Pathological infant cry analysis using wavelet packet transform and probabilistic neural network
Expert Systems with Applications: An International Journal
Classification of Speech Dysfluencies Using LPC Based Parameterization Techniques
Journal of Medical Systems
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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.