Receiver operating characteristics measure for the recognition of stuttering dysfluencies using line spectral frequencies

Abstract

Stuttering is a motor-speech disorder that has features in common 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, emotions, and genetic systems. This study used Line Spectral Frequency (LSF) for 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 the database in this research. These recordings were from people of ages ranging from 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.