Hybrid PSO-ANFIS for Speaker Recognition

Samiya Silarbi, Redouane Tlemsani and Abderrahmane Bendahmane
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Samiya Silarbi: SIMPA Laboratory, University of Sciences and Technology in Oran Mohammed Boudiaf, Bir El Djir, Algeria
Redouane Tlemsani: SIMPA Laboratory, University of Sciences and Technology in Oran Mohammed Boudiaf, Bir El Djir, Algeria
Abderrahmane Bendahmane: SIMPA Laboratory, University of Sciences and Technology in Oran Mohammed Boudiaf, Bir El Djir, Algeria

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2021, vol. 15, issue 2, 83-96

Abstract: This paper introduces an evolutionary approach for training the adaptive network-based fuzzy inference system (ANFIS). The previous works are based on gradient descendent (GD); this algorithm converges very slowly and gets stuck down at bad local minima. This study applies one of the swarm intelligent branches, named particle swarm optimization (PSO), where the premise parameters of the rules are optimized by a PSO, and the conclusion part is optimized by least-squares estimation (LSE). The hybrid PSO-ANFIS model is performed for speaker recognition on CHAINS speech dataset. The results obtained by the hybrid model showed an improvement on the accuracy compared to similar ANFIS based on gradient descendent optimization.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:15:y:2021:i:2:p:83-96

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