@device(postscript) @libraryfile(Mathematics10) @libraryfile(Accents) @style(fontfamily=timesroman,fontscale=11) @pagefooting(immediate, left "@c", center "@c", right "@c") @heading(Learning Models of Speaker Variation) @heading(CMU-CS-96-135) @center(@b(Michael John Witbrock)) @center(July 1996 - Ph.D. Thesis) @center(FTP: Unavailable) @blankspace(1) @begin(text) Speaker based variability is an important component of the speech signal, whether it is regarded as a nuisance, impeding speech recognition, or a goal, improving speech synthesis. Although many speech recognisers attempt to avoid errors caused by speaker variation, and a few synthesisers attempt to produce a wide range of voices, these efforts tend to be narrowly focused on the task at hand, rather than based on a general model of the variation. What work has been done on modelling variability itself, on the other hand, has mainly aimed at understanding specific linguistic events, rather than at providing an implementation that is practical. This thesis attempts to bridge the gap between these two approaches, by using statistical and connectionist techniques to separate out, and to model, the speaker variability component of the speech signal. A number of these models are built and examined for speaker specificity and speed of convergence. Two applications for speaker models are studied with mixed results: speaker adaptation without parameter reestimation for recognition, and mimicry by transforming the voice personality of synthetic speech. @blankspace(2line) @begin(transparent,size=10) @b(Keywords:@ )@c @end(transparent) @blankspace(1line) @end(text) @flushright(@b[(198 pages)])