Visualization of normal and pathological speech data

December 13-15, 2007: Firenze, Italy, ed. by C. Manfredi, ISBN 978 88-8453-673-3 (print) ISBN 978-88-8453-674-7 (online) © Firenze university press, 2007. Abstract: Techniques for the visualization of highdimensional 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.