METHODS OF SIZE REDUCTION TO INCREASE THE EFFICIENCY OF PERSONAL IDENTIFICATION BY VOICE
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Abstract:
This paper addresses the problem of efficient speaker recognition on resource-constrained devices. The focus is placed on reducing memory and computational costs while preserving the discriminative power of the feature set. To achieve this, dimensionality reduction techniques such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and the Genetic Algorithm (GA) were applied. Experimental results demonstrate that these approaches significantly reduce memory usage and computational complexity while maintaining high recognition accuracy. The proposed methodology is particularly suitable for mobile devices and real-time systems with limited resources
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