Journal of Computational and Applied Mathematics vol:234 issue:4 pages:1303-1311
International congress on computational and applied mathematics - ICCAM-2008 edition:13 location:Ghent, Belgium date:7-11 July 2008
The Minimum Classification Error (MCE) criterion is a well-known criterion in pattern classification systems. The aim of MCE training is to minimize the resulting classification error when trying to classify a new data set. Usually, these classification systems use some form of statistical model to describe the data. These systems usually do not work very well when this underlying model is incorrect. Speech recognition systems traditionally use Hidden Markov Models (HMM) with Gaussian (or Gaussian mixture) probability density functions as their basic model. It is well known that these models make some assumptions that are not correct. In example based approaches, these statistical models are absent and are replaced by the pure data. The absence of statistical models has created the need for parameters to model the data space accurately. For this work, we use the MCE criterion to create a system that is able to work together with this example based approach. Moreover, we extend the locally scaled distance measure with sparse, block diagonal weight matrices resulting in a better model for the data space and avoiding the computational load caused by using full matrices. We illustrate the approach with some example experiments on databases from pattern recognition and with speech recognition.
Matton M., Van Compernolle D., Cools R., ''Minimum classification error training in example based speech and pattern recognition using sparse weight matrices'', Journal of computational and applied mathematics, vol. 234, no. 4, pp. 1303-1311, June 2010 (Proceedings of the 13th international congress on computational and applied mathematics - ICCAM-2008, July 7-11, 2008, Ghent, Belgium).