Phoneme recognition using time-dependent versions of self-organizing maps

Authors:
J. Kangas
Affiliations:
Lab. of Inf. & Comput. Sci., Helsinki Univ. of Technol., Espoo, Finland
Venue:
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Year:
1991

Citing 0
Cited 3

Hybrid neural network and case based reasoning system for Web user behavior clustering and classification

International Journal of Hybrid Intelligent Systems
Image compression by a time enhanced self organizing map

CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Residual activity in the neurons allows SOMs to learn temporal order

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Two modifications of the self-organizing map (SOM) are proposed that, unlike the original algorithm, take into account time-dependent features of the input signal. In the first, a time average of a sequence of responses of one SOM is found, and this is recognized by another SOM. In the second, successive input patterns are concatenated together and recognized by the SOM. Comparing the results to those of a recognition system utilizing the original SOM, it was found that one could improve the recognition of isolated phonemes from 10.4% of errors to 7.0% and 5.0% of errors for the integration model and concatenation model, respectively. The improvement in a full-scale system where phoneme segments are also to be located is from 9.2% of errors to 8.2% and 7.6% of errors for the new methods, respectively.