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A histogram based speaker identification technique

Feature extraction has the capability to improve the performance of speaker identification systems. This paper proposes two new techniques for speaker identification based on utilizing a reduced set of the features generated from the Mel Frequency Cepstral Coefficient method (MFCC). These techniques are based on histograms for the features using pre-defined interval lengths. The first technique builds a histogram for all data in the feature vectors for each speaker while the second technique builds a histogram for each feature column in the feature set of each speaker. Speaker identification is based on the Euclidian distance measure.