Using the topology-preserving properties of SOFMs in speech recognition

Self-organizing feature maps (SOFMs) are used as speech feature extractors followed by a classifier based on multilayer feedforward networks. Usually SOFMs have been used in speech recognition as static pattern classifiers or vector quantizers, ignoring their property of preserving the local topology of input pattern space. Here, the topological ordering of the acoustic speech data in the SOFM is utilized to form trajectories in the map which are then fed into a classifier. Viewing the trajectories at multiple resolution levels, feature vectors are formed that take contextual information into account. Experiments with such feature vectors indicate that better accuracies can be obtained than by using a simple SOFM classifier based on instantaneous acoustic features