Correntropy-based linear prediction for voice inverse filtering

Voice inverse filtering analysis comprises different methods for the non-invasive estimation of glottal airflow from a speech signal, thus bringing forth relevant information about the vocal function and acoustic excitation during voiced phonation. Most inverse filtering strategies consider a parametric source-filter model of phonation and variants of linear prediction to adjust the model coefficients. However, classical linear prediction is susceptible to impulse-like acoustic excitations produced by abrupt glottal closures. Robust alternatives have been proposed that apply a time-domain weighting function to de-emphasize the detrimental contribution of the impulse-like glottal events. The present study introduces the maximum correntropy criterion-based linear prediction for voice inverse filtering. This method takes advantage of the correntropy -a non-linear localized similarity measure inherently insensitive to outliers- to implement a robust weighted linear prediction, where the weighting function is adjusted iteratively through a speech-data-guided optimization scheme. Simulations show that the proposed method naturally overweights samples in the glottal closed phase, where the phonation model is more accurate, without being necessary any prior information about the closure instants. It is further shown that maximum correntropy criterion-based linear prediction improves inverse filtering analysis in terms of the smoothness of estimated glottal waveforms, and the spectral relevance of the vocal tract filter.