The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Feature selection from huge feature sets in the context of computer vision
Feature selection from huge feature sets in the context of computer vision
A tutorial on text-independent speaker verification
EURASIP Journal on Applied Signal Processing
A Novel Text-Independent Speaker Verification System Using Ant Colony Optimization Algorithm
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Text feature selection using ant colony optimization
Expert Systems with Applications: An International Journal
A novel ACO-GA hybrid algorithm for feature selection in protein function prediction
Expert Systems with Applications: An International Journal
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Feature selection with particle swarms
CIS'04 Proceedings of the First international conference on Computational and Information Science
Feature selection for dimensionality reduction
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Robust Text-Independent Speaker Verification Using Genetic Programming
IEEE Transactions on Audio, Speech, and Language Processing
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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.