New Contribution to Adaptive Temporal Radial Basis Function Applied on TIMIT Corpus

Introduction A successful speech recognition system has to determine features not only present in the input pattern at one point in time, but also features of input pattern changing over time ( e.g., Berthold, 1994; Benyettou, 1995). In network design, great importance must be attributed to correct choice of the number of hidden neurons, which helps avoiding problems of overfitting and contributes to reduce the time required for the training without significantly affecting the network performances (e.g., Colla & Reyneri & Sgarbi, 1999), but never looking to architecture adapting effect according to input. The goal to combine the approach of the RBF with the shift invariance features of the TDNN, can be get a new robust model, this is named temporal radial basis function “TRBF” (e.g., Mesbahi & Benyettou, 2003), but to be more efficient, we have adapt these networks so that they come more dynamic according to their behaviour and features of the object has study. It can be goes more clearly in continuous speech. Therefore in object to obtain an Adaptive TRBF, we must adapt the TRBF networks, consequently it was necessary to develop an algorithm that permits to solve this type of problem, this algorithm is called “DOLS” which means Dynamic Orthogonal Least Square, that will be presented in this paper.