Nonlinear Dynamics for Hypernasality Detection in Spanish Vowels and Words

A novel technique for characterizing hypernasal vowels and words using nonlinear dynamics is presented considering different complexity measures that are mainly based on the analysis of the time-delay embedded space. After the characterization stage, feature selection is performed by means of two different strategies: principal components analysis and sequential floating feature selection. The final decision about the presence or absence of hypernasality is carried out using a Soft Margin-Support Vector Machine. The database used in the study is composed of the five Spanish vowels uttered by 266 children, 110 healthy and 156 labeled as hypernasal by a experienced voice therapist. The database also includes the words /coco/ and /gato/ uttered by 119 children; 65 of which were diagnosed as hypernasal and the rest 54 as healthy. The results are presented in terms of accuracy, sensitivity and specificity. ROC curves are also included as a widely accepted way to measure the performance of a detection system. The experiments show that the proposed methodology achieves an accuracy of up to 92.08 % using, together, the best subset of features extracted from every vowel and 89.09 % using the combination of the most relevant features in the case of words.