Open AccessRECEIVER OPERATING CHARACTERISTICS MEASURE FOR THE RECOGNITION OF STUTTERING DYSFLUENCIES USING LINE SPECTRAL FREQUENCIES
Author(s) -
Nahrul Khair bin Alang Md. Rashid,
Sabur Ajibola Alim,
Nik Nur Wahidah Nik Hashim,
Wahju Sediono
Publication year - 2017
Publication title -
iium engineering journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.141
H-Index - 6
eISSN - 2289-7860
pISSN - 1511-788X
DOI - 10.31436/iiumej.v18i1.578
Subject(s) - stuttering , multilayer perceptron , psychology , artificial intelligence , speech recognition , dystonia , sensitivity (control systems) , audiology , receiver operating characteristic , computer science , artificial neural network , machine learning , developmental psychology , medicine , neuroscience , engineering , electronic engineering
Stuttering is a motor-speech disorder, having common features with other motor control disorders such as dystonia, Parkinson’s disease and Tourette’s syndrome. Stuttering results from complex interactions between factors such as motor, language, emotional and genetic. This study used Line Spectral Frequency (LSF) for the feature extraction, while using three classifiers for the identification purpose, Multilayer Perceptron (MLP), Recurrent Neural Network (RNN) and Radial Basis Function (RBF). The UCLASS (University College London Archive of Stuttered Speech) release 1 was used as database in this research. These recordings were from people of ages 12y11m to 19y5m, who were referred to clinics in London for assessment of their stuttering. The performance metrics used for interpreting the results are sensitivity, accuracy, precision and misclassification rate. Only M1 and M2 had below 100% sensitivity for RBF. The sensitivity of M1 was found to be between 40 & 60%, therefore categorized as moderate, while that of M2 falls between 60 & 80%, classed as substantial. Overall, RBF outperforms the two other classifiers, MLP and RNN for all the performance metrics considered.