The application of support vector machine for speech classification

For the classical statistical classification algorithms the probability distribution models are known. However, in many real life applications, such as speech recognition, there is not enough information about the probability distribution function. This is a very common scenario and poses a very serious restriction in classification. Support Vector Machines (SVMs) can help in such situations because they are distribution free algorithms that originated from statistical learning theory and Structural Risk Minimization (SRM). In the most basic approach SVMs use linearly separating Hyperplanes to create classification with maximal margins. However in application, the classification problem requires a constrained nonlinear approach to be taken during the learning stages, and a quadratic problem has to be solved. For the case where the classes cannot be linearly separable due to overlap, the SVM algorithm will transform the original input space into a higher dimensional feature space, where the new features are potentially linearly separable. In this paper we present a study on the performance of these classifiers when applied to speech classification and provide computational results on phonemes from the TIMIT database.