Self-Organizing Maps and Learning Vector Quantization forFeature Sequences

Authors:
Panu Somervuo;Teuvo Kohonen
Affiliations:
Helsinki University of Technology, Neural Networks Research Centre, P.O. Box 2200, FIN-02015-HUT, Finland;Helsinki University of Technology, Neural Networks Research Centre, P.O. Box 2200, FIN-02015-HUT, Finland
Venue:
Neural Processing Letters
Year:
1999

Citing 2
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Abstract

The Self-Organizing Map (SOM) and LearningVector Quantization (LVQ) algorithms are constructed in this work forvariable-length and warped feature sequences. The novelty is to associate an entire feature vector sequence,instead of a single feature vector, as a model with each SOM node.Dynamic time warping is used to obtain time-normalizeddistances between sequences with different lengths. Starting withrandom initialization, ordered feature sequence maps then ensue, andLearning Vector Quantization can be used to fine tune the prototypesequences for optimal class separation. The resulting SOM models, theprototype sequences, can then be used for the recognition as well assynthesis of patterns. Good results have been obtained inspeaker-independent speech recognition.