Automatic Speaker Gender Identification for the German Language

— Authentication systems necessitate transmission, design and classification of biometric data in a secure manner. Moreover, in voice process of biometric can be obtained successful results by determining gender of speaker. In this study, the aim was to designed system taking German sound forms and properties for automatic recognition gender of speaker. Approximately 2658 German voice samples of words and clauses with differing lengths have been collected from 50 males and 50 females. This voice samples includes more than one word as a word. Features of these voice samples have been obtained using MFCC (Mel Frequency Cepstral Coefficients). Feature vectors of the voice samples obtained have been trained with such methods as Hidden Markov Model, Dynamic Time Warping and Artifical Neural Network. In the test phase, gender of a given voice sample has been identified taking the trained voice samples into consideration. Results and performances of the algorithms employed in the study for classification have been also demonstrated in a comparative manner

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