Sinusoidal model-based hypernasality detection in cleft palate speech using CVCV sequence

Abstract

Hypernasality in the speech of children with cleft palate is a consequence of velopharyngeal insufficiency. The spectral analysis of hypernasal speech shows the presence of nasal formants and anti-formants in the spectrum which affects the harmonic-intensity. The nasal formants increase whereas the anti-formants decrease the magnitude of harmonics around its location of addition. Hence, the spectrum of hypernasal and normal speech is different from each other. To capture the spectral difference, three features namely, normalized harmonic amplitude (NHA), harmonic amplitude ratio (HAR), and prominent harmonics frequency (PHF) are proposed in this work. NHA feature is the magnitude of harmonics after their normalization with respect to the maximum magnitude, HAR feature is the relative magnitude of harmonics with respect to their previous harmonics, and the PHF feature is the frequencies of prominent harmonics in the spectrum. The combination of three features gives an accuracy of 82.46%, 87.89%, 84.25% for /a/, /i/ and /u/ vowels respectively for the detection of hypernasality using support vector machine classifier.