Particle Swarm Optimization for Feature Selection in Speaker Verification

  • Shahla Nemati
  • Mohammad Ehsan Basiri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)


The problem addressed in this paper concerns the feature subset selection for an automatic speaker verification system. An effective algorithm based on particle swarm optimization is proposed here for discovering the best feature combinations. After feature reduction phase, feature vectors are applied to a Gaussian mixture model which is a text-independent speaker verification model. The performance of proposed system is compared to the performance of a genetic algorithm-based system and the baseline algorithm. Experimentation is carried out, using TIMIT corpora. The results of experiments indicate that with the optimized feature subset, the performance of the system is improved. Moreover, the speed of verification is significantly increased since by use of PSO, number of features is reduced over 85% which consequently decrease the complexity of our ASV system.


Particle Swarm optimization (PSO) Feature Selection (FS) Speaker Verification Gaussian Mixture Model (GMM) Genetic Algorithm (GA) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shahla Nemati
    • 1
    • 2
  • Mohammad Ehsan Basiri
    • 1
    • 2
  1. 1.Arsanjan Branch, farsYoung Research Club, Islamic Azad UniversityIran
  2. 2.Department of Computer Engineering, Faculty of EngineeringUniversity of IsfahanIsfahanIran

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