Automatic identification of hypernasality in normal and cleft lip and palate patients with acoustic analysis of speech.

Hypernasality is seen in cleft lip and palate patients who had undergone repair surgery as a consequence of velopharyngeal insufficiency. Hypernasality has been studied by evaluation of perturbation, noise measures, and cepstral analysis of speech. In this study, feature extraction and analysis were performed during running speech using six different sentences. Jitter, shimmer, Mel frequency cepstral coefficients, bionic wavelet transform entropy, and bionic wavelet transform energy were calculated. Support vector machines were employed for classification of data to normal or hypernasal. Finally, results of the automatic classification were compared with true labels to find accuracy, sensitivity, and specificity. Accuracy was higher when Mel frequency cepstral coefficients were combined with bionic wavelet transform energy feature. In the best case, accuracy of 85% with sensitivity of 82% and specificity of 85% was obtained. Results prove that acoustic analysis is a reliable method to find hypernasality in cleft lip and palate patients.