A mapping neural network and its application to voiced-unvoiced-silence classification

A mapping neural network which combines unsupervised and supervised training is described and its application to the classification of segments of speech to voiced, unvoiced, and silence (V-UV-S) is demonstrated through computer simulations. The network uses a dynamic variation of competitive learning in the unsupervised layer followed by a supervised associative layer. When used to solve the V-UV-S classification problem, the network outperforms a network based on the frequency sensitive competitive learning