In this paper, we figure out the use of appended jitter and shimmer speech features for closed set text

independent speaker identification system. Jitter and shimmer features are extracted from the

fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency

Cepstral Coefficients (MFCCs) for human speech and MFCC-GC which integrate the Gammachirp

filterbank instead of the Mel scale. Hidden Markov Models (HMMs) with Gaussian Mixture Models

(GMMs) state distributions are used for classification. Our approach achieves substantial performance

improvement in a speaker identification task compared with a state-of-the-art robust front-end in a

clean condition.