Gammatone frequency cepstral coefficients for speaker identification over VoIP networks

Over the past several years, the Mel-Frequency Cepstral Coefficients (MFCCs) has become the state-of-the-art approach for features extraction in text-independent speaker recognition applications. However, the recently introduced Gammatone Frequency Cepstral Coefficients (GFCC) has shown a promising recognition performance in such speaker recognition applications, especially in noisy acoustical environments. In this paper, The Gammatone Frequency Cepstral Coefficients are studied and evaluated text-independent speaker identification task over VoIP Networks. The study comprises the exploration of the various parameters included in the calculation process of the Gammatone Frequency Cepstral Coefficients. The GFCCs features were tested and accessed under a Gaussian mixture model (GMM)-based speaker identification system, which represents the speaker modeling state-of-art approach in contemporary text-independent speaker identification systems.