Some experiments on speaker-independent isolated digit recognition using SVM classifiers

Speech recognition is essentially a problem of pattern classification, but the high dimensionality of the sequences of speech feature vectors has prevented researchers to propose a straightforward classification scheme for ASR. Support Vector Machines (SVMs) are state-of-the-art tools for linear and nonlinear knowledge discovery [14], [18]. Being based on the maximum margin classifier, which can be regarded as the common sense solution, the SVM is able to outperform classical classifiers in the presence of high dimensional data even when working with nonlinear machines. The SVM “philosophy” basically states that the only available information for constructing the classifier are the training samples. Therefore, in those applications in which a priori knowledge or structure is known, the SVM might not be as powerful as other machine learning techniques which can benefit form this information. Some work has been done in this direction [7], but still there are open issues that need to be addressed.