Detecting pathological speech using contour modeling of harmonic-to-noise ratio

This paper proposes a new feature extraction method for automatically detecting pathological voice in a normal conversation scenario. Unlike conventional approaches that utilize the static harmonic-to-noise ratio (HNR) characteristics of sustained vowel, the proposed method considers the dynamic movements of articulatory organs depending on the types of phonations. Assuming those movements reflect the health status of subjects, the proposed method utilizes the characteristics of HNR contour within a single sentence-level speech signal. Experimental results show that the proposed method reduces the classification error rate by 35.2 % (relative) compared to the conventional method.