A Hybrid Model of MFCC/MSFLA for Speaker Recognition
[1]
Majida Ali Abed, College of Computers Sciences & Mathematics, University of Tikrit, Tikrit, Iraq.
[2]
Hamid Ali Abed Alasadi, Computers Sciences Department, Education for Pure Science College, University of Basra, Basra, Iraq.
In this paper, speaker recognition system is optimized based on one of Swarm Intelligence Algorithm called Modified Shuffle Frog Leaping Algorithm (MSFLA) with Cepstral analysis and the Mel Frequency Cepstral Coefficients (MFCC) feature extraction approach. In this algorithm Search has been applied on speaker recognition systems and voice. Thus by applying this algorithm, the process of speaker recognition is optimized by a fitness function by matching of voices being done on only the extracted optimized features produced by the MSFLA. The recognition accuracy for various noise conditions (white Gaussian noises, car-noises and B-noises) with same dataset are 94.02%, 96.78% and 84.33%, respectively, using a Hybrid model of MFCC/MSFLA.
Speaker Recognition, Mel Frequency Cepstral Coefficients (MFCCs), Modified Shuffled Frog Leaping Algorithm (MSFLA)
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