Speaker Identification using Multi Methods of Features Extraction
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Abstract
The primary challenge in identifying speakers is extracting recognition features from speech signals to optimize classification algorithms' performance. Several methods are proposed in this article for extracting differential features from an audio signal in order to classify the speaker. The following methods were used to obtain the features of the audio signal in this approach: Power Spectral Density (PSD), Short Term Energy (STE), Fast Fourier Transform (FFT), Hue Seven Moment Invariants method (HSMI), Mel Frequency Cepstrum Coefficients (MFCC), cross-correlation estimates of MFCC (XCORR), and Linear predictive coding (LPC). The classification methods in this paper are the artificial neural network (ANN), the Euclidean distance, and the autocorrelation, where the results obtained from the experiments showed that the accuracy rate is more than 96%.
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