In this paper, we propose automatic speaker recognition using circular DFT (Discrete Fourier Transform) sectors. In the first method, the DFT of the speech signal is taken and feature vectors are extracted by dividing the complex DFT spectrum into circular sectors and then taking the weighted density count of the number of points in each of these sectors. In the second method, the speech signal is first divided into frames and these frames are arranged as columns of a matrix. DFT is applied to this matrix and then the complex DFT plane is similarly divided into circular sectors and feature vectors are extracted as in the first method. The results show that this approach gives fairly good speaker recognition (70 % -80%) for the Ist method. Also the results improve as the circular sectors are further divided into quadrants. For the IInd method the accuracy does not improve as the circular sectors are further divided. The best accuracy is obtained with 7 circular sectors.

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Discrete Fourier Transform (DFT) circular sectors Euclidean distance