A robust Speaker Identification System based on the combination of GFCC and MFCC methods

Speech processing applications have become integral components across various domains of modern life. The design and preparation of a reliable recognition system rely heavily on the availability of suitable speech databases. While numerous speech databases exist for English and other languages, the availability of comprehensive resources for Arabic language remains limited. In light of this, we conducted a systematic review aiming to identify, analyse, and classify existing Modern Standard Arabic speech databases. Through our review, we identified 27 publicly available databases and analysed an additional 80 subjective databases. These databases were thoroughly studied, classified based on their characteristics, and subjected to a detailed analysis of research trends in the field. This paper provides a comprehensive discussion on the diverse speech databases developed for various speech processing applications. It sheds light on the purposes and unique characteristics of Arabic speech databases, enabling researchers to easily access suitable resources for their specific applications. The findings of this review contribute to bridging the gap in available Arabic speech databases and serve as a valuable resource for researchers in the field.

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A robust Speaker Identification System based on the combination of GFCC and MFCC methods

Cited by 6 publications

References 7 publications

M-GFCC: Audio Copy-Move Forgery Detection Algorithm Based on Fused Features of MFCC and GFCC

M-GFCC: Audio Copy-Move Forgery Detection Algorithm Based on Fused Features of MFCC and GFCC

Gunshots Localization and Classification Model Based on Wind Noise Sensitivity Analysis Using Extreme Learning Machine

Modern Standard Arabic Speech Corpora: A Systematic Review

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