Visualization of Normal and Pathological Speech Data

JOHN C. GODDARD; FABIOLA M. MARTINEZ; GASTÓN SCHLOTTHAUER; MARÍA E. TORRES; HUGO L. RUFINER

Firenze, Italy

Workshop; Proc. of the 5th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA 2007); 2007

Techniques for the visualization of high-dimensional data are common in exploratory data analysis and can be very useful for gaining an intuition into the structure of a data set. The classical method of principal component analysis is the one most often employed, however in recent years a number of other nonlinear techniques have been introduced. In the present paper, principal component analysis, and two newer methods, are applied to a set of speech data and their results are compared.