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 benefits from an adaptive decomposition method, the Matching Pursuit (MP) algorithm, to decompose involved signals to some predefined atoms. Then, the attributes of the obtained atoms assigned to the speech signal converts to a final 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 classification rate by 4.98% as compared to the EWPD feature vector.