Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
Using genetic algorithms to estimate confidence intervals for missing spatial data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
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This paper presents Genetic Algorithms and Simulated Annealing (GASA) based on feature extraction of speech signal and comparison with traditional Linear Predictive Coding (LPC) methods. The performance of each method is analyzed for ten speakers with independent text speaker verification database from Center for Spoken Language Understanding (CSLU) which was developed by Oregon Graduate Institute (OGI). The GASA algorithm is also analyzed with constant population size for different generation numbers, crossover and mutation probabilities. When compared with the Mean Squared Error (MSE) of the each speech signal for each method, all simulation results of the GASA algorithm are more effective than LPC methods.