Speech trajectory recognition in SOFM by using Bayes theorem

Trajectory of a speech signal on a self-organizing feature map (SOFM) is usually obtained by concatenating the cells with peak neural excitation given each input vector. This usually causes unsmooth trajectory of speech. We introduce a new method, solidly grounded on Bayes rule, to find the response trajectory in SOFM. It takes into account not only the present response of the cells given input vector but also the a priori information of the response in SOFM for each class. To test the effectiveness of this method, a Multilayer Perceptron (MLP) is used to classify the trajectory to the class it belongs to. It will be shown experimentally that with our new method the recognition rate is increased from 91.6% to 96.2%