Comparison of speech parameterization techniques for the classification of speech disuencies

Stuttering assessment through the manual classication of speech disuencies is subjective, inconsistent, time-consuming, and prone to error. The aim of this paper is to compare the effectiveness of the 3 speech feature extraction methods, mel-frequency cepstral coefficients, linear predictive coding (LPC)-based cepstral parameters, and perceptual linear predictive (PLP) analysis, for classifying 2 types of speech disuencies, repetition and prolongation, from recorded disuent speech samples. Three different classiers, the k-nearest neighbor classifier, linear discriminant analysis-based classifier, and support vector machine, are employed for the classication of speech disuencies. Speech samples are taken from the University College London Archive of Stuttered Speech and stuttered events are identied through manual segmentation. A 10-fold cross-validation method is used for testing the reliability of the classifier results. The effect of the 2 parameters (LPC order and frame length) in the LPC- and PLP-based methods on the classification results is also investigated. The experimental results reveal that the proposed method can be used to help speech language pathologists in classifying speech disuencies.