Feature Extraction of Speech Signal by Genetic Algorithms-Simulated Annealing and Comparison with Linear Predictive Coding Based Methods
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.
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- 3.Deller, J.R., Proakis, J.G., Hansen, J.H.L.: Discrete-Time Processing of Speech Signal. Macmillan Pub. Co, Basingstoke (1993)Google Scholar
- 10.Sailesh Babu, G.S., Bhagwan Das, D., Patvardhan, C.: A Hybrid Stochastic Search Approach for Unit Commitment with Hard Reserve Constraints. In: IEEE Power India Conf., pp. 355–362 (2006)Google Scholar
- 14.Pulido, G.T.: On the Use of Self-Adaptation and Elitism for Multiobjective Particle Swarm Optimization. PhD Thesis in Electrical Eng. Department Computer Science Section, Centro de Investigación y Estudios Avanzados del Inst. Politécnico Nacional (2005)Google Scholar