Automatic Detection Algorithm of Pharyngeal Fricatives in Cleft Palate Speech Based on LPIF and Feature Selection

Abstract

Cleft palate is one of common congenital malformations that has huge impacts on the physical and psychological health of patients. Pharyngeal fricative in cleft palate speech is a kind of common compensatory articulations, which is produced by retracting tongue position to posterior pharyngeal wall and narrowing velopharyngeal opening. In this paper, based on voice mechanism of pharyngeal fricatives in cleft palate speech, linear prediction inverse filter was used to extract glottal waveform and estimate vocal tract model coefficients. Then four features were extracted, including pitch period, glottal flow derivative waveform, vocal tract area and vocal tract gain. The cross-correlation function was used to calculate the correlation between features. Glottal flow derivative waveform was removed since it had strong correlation with the others. KNN classifier was applied to realize the automatic pharyngeal fricatives detection in cleft palate speech, which reaches the detection accuracy of 98.40%.