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One of the major parts of the voice recognition field is the choice ofacoustic features which have to be robust against the variability of the speechsignal, mismatched conditions, and noisy environments. Thus, different speechfeature extraction techniques have been developed. In this paper, weinvestigate the robustness of several front-end techniques in Arabic speakeridentification. We evaluate five different features in babble, factory andsubway conditions at the various signal to noise ratios (SNR). The obtainedresults showed that two of the auditory feature i.e. gammatone frequencycepstral coefficient (GFCC) and power normalization cepstral coefficients(PNCC), unlike their combination performs substantially better than aconventional speaker features i.e. Mel-frequency cepstral coefficients (MFCC).

A Study of Acoustic Features in Arabic Speaker Identification under Noisy Environmental Conditions