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On the Use of Long-Term Average Spectrum in Automatic Speaker Recognition

State-of-the-art automatic speaker recognition systems use mel-frequency cepstral coefficients (MFCC) features to describe the spectral properties of speakers. In forensic phonetics, the long-term average spectrum (LTAS) has been used for the same purpose. LTAS provides an intuitive graphical representation which can be used to visualize and quantify speaker differences. However, few studies have reported the use of LTAS in automatic speaker recognition. Thus, the purpose of this paper is to systematically study how to use the LTAS in automatic speaker recognition. We will also find out whether it provides additional discriminative information in respect to the MFCC-based system.