A Study of Acoustic Features in Arabic Speaker Identification under Noisy Environmental Conditions
This work addresses speaker identification challenges in noisy conditions for Arabic speech processing, but it is incremental as it compares existing features without introducing new methods.
The study evaluated the robustness of five acoustic features for Arabic speaker identification in noisy environments, finding that GFCC and PNCC performed substantially better than conventional MFCC features.
One of the major parts of the voice recognition field is the choice of acoustic features which have to be robust against the variability of the speech signal, mismatched conditions, and noisy environments. Thus, different speech feature extraction techniques have been developed. In this paper, we investigate the robustness of several front-end techniques in Arabic speaker identification. We evaluate five different features in babble, factory and subway conditions at the various signal to noise ratios (SNR). The obtained results showed that two of the auditory feature i.e. gammatone frequency cepstral coefficient (GFCC) and power normalization cepstral coefficients (PNCC), unlike their combination performs substantially better than a conventional speaker features i.e. Mel-frequency cepstral coefficients (MFCC).