Abstract
Automatic hypernasality detection in children with Cleft Lip and Palate is made considering five Spanish vowels. Characterization is performed by means of some acoustic and noise features, building a representation space with high dimensionality. Most relevant features are selected using Principal Components Analisis and linear correlation in order to enable clinical interpretation of results and achieving spaces with lower dimensions per vowel. Using a Linear-Bayes classifier, success rates between 80% and 90% are reached, beating success rates achived in similiar studies recently reported. © 2011 Springer-Verlag Berlin Heidelberg.
Author supplied keywords
Cite
CITATION STYLE
Rendón, S. M., Orozco Arroyave, J. R., Vargas Bonilla, J. F., Arias Londoño, J. D., & Castellanos Domínguez, C. G. (2011). Automatic detection of hypernasality in children. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6687 LNCS, pp. 167–174). https://doi.org/10.1007/978-3-642-21326-7_19
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
