Non-linear filtering in reproducing Kernel Hilbert Spaces for noise-robust speaker verification

In this paper, we present a non-linear filtering approach for extracting noise-robust speech features that can be used in a speaker verification task. At the core of the proposed approach is a time-series regression using reproducing kernel Hilbert space (RKHS) based methods that extracts discriminatory non-linear signatures while filtering out the non-informative noise components. A linear projection is then used to map the characteristics of the RKHS regression function into a linear-predictive vector which is then presented as an input to a back-end speaker verification engine. Experiments using the YOHO speaker verification corpus demonstrate that a recognition system trained using the proposed features demonstrate consistent improvements over an equivalent Mel-frequency cepstral coefficients (MFCCs) based verification system for signal-to-noise levels ranging from 0-30 dB.