Intelligibility detection of pathological speech using asymmetric sparse kernel partial least squares classifier

Dong-Yan Huang, Minghui Dong, Haizhou Li

Abstract: Pathological speech usually refers to the voice disorders resulting from atypicalities in voice and/or in the articulatory mechanisms due to disease, illness or other physical problem in the speech production system. It may increase unhealthy social behavior and voice abuse, and dramatically affect the patients' quality of life. Therefore, automatic intelligibility detection of pathological speech has an important role in the opportune treatment of pathological voices. This paper proposes to use asymmetric sparse kernel partial least squares classifier (ASKPLSC) for intelligibility detection of pathological speech. The proposed approach achieves an unweighted accuracy (UA) of 74.0%, which is 7.34% relative improvement of baseline system of an UA of 68.90% for the Pathology Sub-Challenge of INTERSPEECH 2012 Speaker Trait Challenge.