Remote diagnosis of unilateral vocal fold paralysis using matching pursuit based features extracted from telephony speech signal

Unilateral Vocal Fold Paralysis (UVFP) is a type of neurogenic laryngeal disorder in which vocal folds of patients do not have their normal behaviors, leading to abnormal talking voices. In this paper, a new noninvasive method for processing telephony speech signals is proposed to remotely diagnose the voice of the patients with UVFP disease. The proposed feature extraction method benets from an adaptive decomposition method, the Matching Pursuit (MP) algorithm, to decompose the involved signals to some predened atoms. Then, the attributes of the obtained atoms assigned to the speech signal converts to a nal feature vector, so called MSDMP. Simulation results indicate the usefulness of the proposed feature vector with respect to a commonly used wavelet based features (EWPD). The MSDMP feature vector has improved the classication rate by 4.98%, as compared to the EWPD feature vector.