Detecting pathological speech using local and global characteristics of harmonic-to-noise ratio

This paper proposes an efficient feature extraction method for automatic diagnosis systems to detect pathological subjects using continuous speech. Since continuous speech contains slow and rapid adjustments of vocal mechanisms which relate to initiations and terminations of voicing, the proposed algorithm utilizes both localized temporal characteristics and histogram-based global statistics of harmonic-to-noise ratio (HNR) to efficiently differentiate the key features from phonetic variation. Experimental results show that the proposed method improves the classification error rate by 11.2% (relative) compared to the conventional method using HNR.