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There are several methods to create visualizations of speech data. All of them, how-ever, lack the ability to remove microphone-dependent distortions. We examined the useof Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and theCOmprehensive Space Map of Objective Signal (COSMOS) method in this work. To solvethe problem of lacking microphone independency of PCA, LDA, and COSMOS, we presenttwo methods to reduce the inuence of the recording conditions on the visualization. Therst one is a rigid registration of maps created from identical speakers recorded underdierent conditions, i.e. dierent microphones and distances. The second method is anextension of the COSMOS method, which performs a non-rigid registration during themapping procedure. As a measure for the quality of the visualization, we computed themapping error which occurs during the dimension reduction and the grouping error as theaverage distance between the representations of the same speaker recorded by dierent mi-crophones. The best linear method in leave-one-speaker-out evaluation is PCA plus rigidregistration with a mapping error of 47% and a grouping error of 18%. The proposedmethod, however, surpasses this even further with a mapping error of 24% and a groupingerror which is close to zero.Keywords: Speech intelligibility, speech and voice disorders, speech evaluation, dimen-sionality reduction, Sammon mapping, QMOS, COSMOS, COmprehensive Space Map ofObjective Signal

QMOS - a Robust Visualization Method for Speaker Dependencies With Diﬀerent Microphones