MFCC and Texture Descriptors based Stuttering Dysfluencies Classification using Extreme Learning Machine

Abstract

Stuttering is a type of speech disorder which results in disrupted flow of speech in the form of unintentional repetitions and prolongation of sounds. Stuttering classification is important for speech pathology treatment and speech therapy techniques which decreases speech disfluency to some extent. In this article, a method for prolongation and repetition classification is presented based on Mel-frequency cepstral coefficients (MFCC) and texture descriptors. Initially, MFCC and filter bank energy (FBE) matrix are computed. Gray level co-occurrence matrix (GLCM) and Gray level run length matrix (GLRLM) textural features are extracted from these matrices. Laplacian score-based feature selection approach is employed to choose relevant features. Finally, extreme learning machine (ELM) is utilized to classify the speech audio event as repetition or prolongation. The algorithm is evaluated using UCLASS database and has achieved improved performance with classification accuracy of 96.36%.