Abstract
A vowel is a sound where air coming from the lungs is not blocked by the mouth or throat. The articulatory features that distinguish different vowel sounds are said to determine the vowel's quality. It is very important to recognize different vowel classes. A vowel recognition model based on improved least squares support vector machine (LSSVM) is presented. In the model, when the LSSVM is used in vowel recognition, it is presented to choose parameter of kernel function on dynamic, which enhances preciseness rate of diagnosis. The Fibonacci symmetry searching algorithm is simplified and improved. The changing rule of kernel function searching region and best shortening step is studied. The best pattern recognition results are obtained by means of synthesizing kernel function searching region and best shortening step. The simulation results show the validity of the vowel recognition model.
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