Sixth International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2009)
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
Full Paper (reprinted with permission from Firenze University Press)
Bibliographic reference. Sarria-Paja, M. / Castellanos-Domínguez, G. / Gaviria-Gómez, N. (2009): "Principal component analysis for HMM-based pathological voice detection", In MAVEBA-2009, 69-72.