This paper presents a methodology for feature selection in dynamic problems based on the analysis of the variation of linear components in acoustic features combined with an estimation of the ratio between a compactness measure to the separation measure. The methodology is applied to the automatic detection of voice disorders by means of stochastic dynamic models: results showed a significant reduction in the number of features, 96.6% of accuracy, and a 62.2% of computational cost reduction.
Index Terms. Dynamic features. HMM, PCA, feature selection, pathological voice, clustering