A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Robust parallel speech recognition in multiple energy bands
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Environmental adaptation with a small data set of the target domain
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Visualization of voice disorders using the sammon transform
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
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We present a novel method for the visualization of speakers which is microphone independent. To solve the problem of lacking microphone independency we present two methods to reduce the influence of the recording conditions on the visualization. The first one is a registration of maps created from identical speakers recorded under different conditions, i.e., different microphones and distances in two steps: Dimension reduction followed by the linear registration of the maps. The second method is an extension of the Sammon mapping method, which performs a non-linear registration during the dimension reduction procedure. The proposed method surpasses the two step registration approach with a mapping error ranging from 17 % to 24 % and a grouping error which is close to zero.