  The recent resurgence of interest in spatio-temporal neural network as speech
recognition tool motivates the present investigation. In this paper an approach
was developed based on temporal radial basis function "TRBF" looking to many
advantages: few parameters, speed convergence and time invariance. This
application aims to identify vowels taken from natural speech samples from the
Timit corpus of American speech. We report a recognition accuracy of 98.06
percent in training and 90.13 in test on a subset of 6 vowel phonemes, with the
possibility to expend the vowel sets in future.
