Hybrid models based on biological approaches for speech recognition

Authors:
Nabil Neggaz;Abdelkader Benyettou
Affiliations:
Laboratory Signal Image Parole (SIMPA), Department of Computer Science, University of Science and Technology of Oran USTO Algeria, Oran Elmnaouer, Algeria 31036;Laboratory Signal Image Parole (SIMPA), Department of Computer Science, University of Science and Technology of Oran USTO Algeria, Oran Elmnaouer, Algeria 31036
Venue:
Artificial Intelligence Review
Year:
2009

Citing 13
Cited 1

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Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)

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A review of clonal selection algorithm and its applications

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Abstract

This paper aims to adapt the Clonal Selection Algorithm (CSA) which is usually used to explain the basic features of artificial immune systems to the learning of Neural Networks, instead of Back Propagation. The CSA was first applied to a real world problem (IRIS database) then compared with an artificial immune network. CSA performance was contrasted with other versions of genetic algorithms such as: Differential Evolution (DE), Multiple Populations Genetic Algorithms (MPGA). The tested application in the simulation studies were IRIS (vegetal database) and TIMIT (phonetic database). The results obtained show that DE convergence speeds were faster than the ones of multiple population genetic algorithm and genetic algorithms, therefore DE algorithm seems to be a promising approach to engineering optimization problems. On the other hand, CSA demonstrated good performance at the level of pattern recognition, since the recognition rate was equal to 99.11% for IRIS database and 76.11% for TIMIT. Finally, the MPGA succeeded in generalizing all phonetic classes in a homogeneous way: 60% for the vowels and 63% for the fricatives, 68% for the plosives.