On Improvements to CI-based GMM SelectionComputer Science Department
Date of Original Version1-1-2005
Abstract or DescriptionGaussian Mixture Model (GMM) computation is known to be one of the most computation-intensive components in speech decoding. In our previous work, context-independent model based GMM selection (CIGMMS) was found to be an effective way to reduce the cost of GMM computation without significant loss in recognition accuracy. In this work, we propose three methods to further improve the performance of CIGMMS. Each method brings an additional 5-10% relative speed improvement, with a cumulative improvement up to 37% on some tasks. Detailed analysis and experimental results on three corpora are presented.
Citation InformationArthur Chan, Mosur Ravishankar and Alexander I Rudnicky. "On Improvements to CI-based GMM Selection" (2005)
Available at: http://works.bepress.com/alexander_rudnicky/7/