A text-independent speaker identification system using PARCOR and AR model

In this work, we propose the partial-correlation (PARCOR) coefficients scheme to model the cross areas of the several cylinders from the vocal tract. By using the relationship of the acoustic impedance proportional to the reciprocal of cross areas, the ratios of cross areas between each neighboring cylinders are used to model a speaker´s vocal tract. The autoregressive model (AR model) is performed on the speech residual signals, that are produced from the inverse vocal tract transform based on the PARCOR, to generate features. These features with the conventional features from the Mel-Frequency Cepstral Coefficient (MFCC) are used for the identification engine of the Gaussian Mixture Model (GMM). According to our computer analyses in the TIMIT speech database, the proposed system can yield better identification performance than the conventional approach.