Automatic detection of hypernasal speech of children with cleft lip and palate from spanish vowels and words using classical measures and nonlinear analysis

This paper presents a system for the automatic detection of hypernasal speech signals based on the combination of two different characterization approaches applied to the five Spanish vowels and two selected words. First one is based on classical features such as pitch period perturbations, noise measures, and Mel-Frequency Cepstral Coefficients (MFCC). Second is based on the non-linear dynamics (NLD) analysis. The most relevant features are selected and sorted according to two techniques: principal components analysis (PCA), and sequential floating feature selection (SFFS). The decision about whether a voice record is hypernasal or healthy is taken using a soft margin - support vector machine (SM-SVM). The experiments are carried out using recordings of the five Spanish vowels and the words /coco/ and /gato/, considering three different set of features: (1) the classical approach, (2) the NLD analysis, and (3) the combination of the classical and NLD measures. In general, the accuracy rates are higher and more stable when the classical and NLD features are combined into the same feature space, indicating that the NLD analysis is a complement for the classical approach.