Voice analysis for personal identification using FFT, machine learning and AI techniques

In this paper a hybrid approach for spectral analysis and voice profiles recognition by techniques on the base of Machine Learning and Artificial Intelligence (AI) have been proposed. For voice-processing procedures was applied the algorithm of Fast Fourier Transformation (FFT) with several different window types, respectively Hamming, 4 Term BHarris, Flat Top and Hanning. The spectral feature extraction is used for pre-processing training sets about k-Nearest Neighbours (k-NN) classification models and Feed-Forward Neural Networks (FFNN) for individual’s personal identity about target group of people. Euclidean, Cityblock, Minkowski and Chebychev metric distances were applied in k-NN model creation. The design models are evaluated through resubstitution and cross-validation techniques. Levenberg- Marquardt learning algorithm was used to FFNN architectures with Linear and Tangent Sigmoid activation functionsin network outputs. High quality k-NN and FFNN models in regard to personal voice identification with level of accuracy achieved 97.68 % and 100.0 % were synthesized.