Lagrangian support vector machines for phoneme classification

We study the performance of binary and multi-category SVMs for phoneme classification. The training process of the standard formulation involves the solution of a quadratic programming problem whose complexity depends on the size of the training set. The large size of speech corpora such as TIMIT limits seriously their practical use in continuous speech recognition tasks, using off the shelf personal computers in a reasonable time. In this paper, we attempt to overcome the above difficulty by using the alternative Lagrangian formulation which only requires the inversion of a matrix whose dimension is proportional to the size of the MFCC sequence of vectors. We provide computational results of all possible binary classifiers (1830) on the TIMIT database which are shown to be competitive in terms of recognition rates (96.8%) with those found in the literature (95.6%). The binary classifiers are introduced in the DAGSVM and voting algorithms to perform multi-category classification on some hand picked subsets from TIMIT corpus.