Speaker independent vowel recognition using neural tree networks

Abstract

A novel approach to the problem of speaker-independent vowel recognition is presented. A novel neural architecture and learning algorithm called neural tree networks (NTNs) are developed. This network uses a tree structure with a neural network at each tree node. The NTN architecture offers a very efficient hardware implementation as compared to MLPs (multilayer perceptrons). The NTN algorithm grows the neurons while learning as opposed to backpropagation, for which the number of neurons must be known before learning can begin. The proposed algorithm is guaranteed to converge on the training set whereas backpropagation can get stuck in local minima. Simulation results on a speaker-independent vowel-recognition task are presented which show that the new method is superior to both the MLP and decision tree methods.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>