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

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 in...

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Main Authors Benhafid, Zhor, Zergat, Kawthar Yasmine, Amrouche, Abderrahmane
Format Journal Article
LanguageEnglish
Published 23.10.2021
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Abstract 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).
AbstractList 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).
Author Benhafid, Zhor
Amrouche, Abderrahmane
Zergat, Kawthar Yasmine
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  givenname: Abderrahmane
  surname: Amrouche
  fullname: Amrouche, Abderrahmane
BackLink https://doi.org/10.48550/arXiv.2110.12304$$DView paper in arXiv
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Title A Study of Acoustic Features in Arabic Speaker Identification under Noisy Environmental Conditions
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