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.

Gammashirp filter.pdf

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