ISSN:
1573-773X
Keywords:
learning vector quantization
;
self-organizing map
;
sequence processing
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract The Self-Organizing Map (SOM) and Learning Vector Quantization (LVQ) algorithms are constructed in this work for variable-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-normalized distances between sequences with different lengths. Starting with random initialization, ordered feature sequence maps then ensue, and Learning Vector Quantization can be used to fine tune the prototype sequences for optimal class separation. The resulting SOM models, the prototype sequences, can then be used for the recognition as well as synthesis of patterns. Good results have been obtained in speaker-independent speech recognition.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1018741720065
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