  The Support Vector Machine (SVM) method has been widely used in numerous
classification tasks. The main idea of this algorithm is based on the principle
of the margin maximization to find an hyperplane which separates the data into
two different classes.In this paper, SVM is applied to phoneme recognition
task. However, in many real-world problems, each phoneme in the data set for
recognition problems may differ in the degree of significance due to noise,
inaccuracies, or abnormal characteristics; All those problems can lead to the
inaccuracies in the prediction phase. Unfortunately, the standard formulation
of SVM does not take into account all those problems and, in particular, the
variation in the speech input. This paper presents a new formulation of SVM
(B-SVM) that attributes to each phoneme a confidence degree computed based on
its geometric position in the space. Then, this degree is used in order to
strengthen the class membership of the tested phoneme. Hence, we introduce a
reformulation of the standard SVM that incorporates the degree of belief.
Experimental performance on TIMIT database shows the effectiveness of the
proposed method B-SVM on a phoneme recognition problem.
