Whitening-Based Feature Space Transformations in a Speech Impediment Therapy System [chapter]

András Kocsor, Róber Busa-Fekete, András Bánhalmi
Text, Speech and Dialogue  
It is quite common to use feature extraction methods prior to classification. Here we deal with three algorithms defining uncorrelated features. The first one is the so-called whitening method, which transforms the data so that the covariance matrix becomes an identity matrix. The second method, the wellknown Fast Independent Component Analysis (FastICA) searches for orthogonal directions along which the value of the non-Gaussianity measure is large in the whitened data space. The third one,
more » ... . The third one, the Whitening-based Springy Discriminant Analysis (WSDA) is a novel method combination, which provides orthogonal directions for better class separation. We compare the effects of the above methods on a real-time vowel classification task. Based on the results we conclude that the WSDA transformation is especially suitable for this task.
doi:10.1007/978-3-540-74628-7_30 dblp:conf/tsd/KocsorBB07 fatcat:tdn7u6mczvcuhm7babcd6lhfyu