Speaker normalization with self-organizing feature maps

An efficient speaker-normalization method based on the mapping of two self-organizing feature maps is developed. The normalization system consists of a reference map trained on the reference speaker´s feature space and a test speaker´s map generated by a special topology maintaining/retraining reference map. The retraining procedure is called ´forced competitive learning ´ (FCL). It allows for an 1:1-exchange of the feature vectors represented by the neurons of the reference map for those of the test map in the operation phase. Pilot tests on a 33-word (including the 10 digits) database have been performed employing a simple HMM-isolated-word recognizer. The evaluation was based on speaker-dependent recognition and has shown an average adaptation efficiency of ρ=0,90. By using topology-preserving feature maps, the method proposed can broadly be applied as a front end to all kinds of VQ-based recognition systems.