Abstract: : For an access control system, which is a speaker identification system based on whispered speech. Speaker identification is a main function of an access control system. Hence, a novel speaker identification system using instantaneous frequencies is proposed. The input speech signals pass through both signal independent and signal dependent filters firstly. Then, we derive the signal’s instantaneous frequencies by applying the Hilbert transform. The analysed instantaneous frequencies are proceeded to be modelled as probability density models. Using these probability density models as the feature in the proposed speaker identification system. Here, compare the use of parametric and nonparametric probability density estimation for instantaneous frequency modeling. Furthermore, propose an approximated probability product kernel support vector machine (APPKSVM). In the APPKSVM, Riemann sum is applied in approximating the probability product kernel. The whisper sounds from the chain speech corpus were used in the experiments. Results of the experiments show the superiority of the existing speaker identification system. For the proposed system using the empirical mode decomposition.

Keywords: MFCC, APPKSVM, FP model, LPCC, IF, EMD, RPS