On the Performance of Hurst-Vectors for Speaker Identification Systems

The performance of Hurst-Vectors (pH feature) for speaker identification systems is presented and discussed in this paper. The pH feature is a vector of Hurst (H) parameters obtained by applying a wavelet-based multi-dimensional estimator (M_dim_wavelets) to the windowed short-time segments of speech. The GMM (Gaussian Mixture Models) and the M_dim_fBm (multi-dimensional fractional Brownian motion) classification systems were considered in the performance analysis. The database—recorded from fixed and cellular phone channels— was uttered by 75 different speakers. The results have shown the superior performance of the M_dim_fBm classifier and that the pH feature aggregates new information on the speaker identity.

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