A combined self-organizing feature map and multilayer perceptron for isolated word recognition

Abstract

A neural network system which combines a self-organizing feature map and multilayer perception for the problem of isolated word speech recognition is presented. A new method combining self-organization learning and K-means clustering is used for the training of the feature map, and an efficient adaptive nearby-search coding method based on the 'locality' of the self-organization is designed. The coding method is shown to save about 50% computation without degradation in recognition rate compared to full-search coding. Various experiments for different choices of parameters in the system were conducted on the TI 20 word database with best recognition rates as high as 99.5% for both speaker-dependent and multispeaker-dependent tests. >