Robust spectral representation using group delay function and stabilized weighted linear prediction for additive noise degradations

In this paper, we propose a robust spectral representation using the group delay (GD) function computed from the stabilized weighted linear prediction (SWLP) coefficients. Temporal weighting of the cost function in linear prediction (LP) analysis with the short-term energy of the speech signal improves the robustness of the resultant spectrum. The additive property of the group delay function provides for better representation of weaker resonances in the spectrum, and thereby improving the robustness of the representation. The SWLP provides robustness in the temporal domain, whereas the GD function provides robustness in the frequency domain. The proposed SWLP-GD representation is shown to be robust against different types of additive noise degradations, compared to the popularly used discrete Fourier transform (DFT) or LP based representations. In a small-scale closed-set speaker recognition experiment, the cepstral features derived from the proposed SWLP-GD spectrum perform better than the traditional mel-cepstral features computed from the discrete Fourier transform (DFT) spectrum under conditions of mismatched degradations.