Feature maps for input normalization and feature integration in a speaker independent isolated digit recognition system

The use of the topology preserving properties of feature maps for speaker-independent isolated digit recognition is discussed. The results of recognition experiments indicate that feature maps can be effectively used for input normalization, which is important for practical implementations of neural-network-based classifiers. Recognition rates can be increased when a third feature map is trained to integrate the responses of two feature maps, each trained with different transducer-level features. Despite the use of a rudimentary classification scheme, recognition rates exceeded 97% for integrated, feature-map-normalized, transducer-level features