Fundamental Frequency Informed Speech Enhancement in a Flexible Statistical Framework
Martin Krawczyk-Becker, Timo Gerkmann · IEEE/ACM Transactions on Audio Speech and Language Processing · 2016
Conventional statistical clean speech estimators, like the Wiener filter, are frequently used for the spectro-temporal enhancement of noise corrupted speech. Most of these approaches estimate the clean speech independently for each time-frequency point, neglecting the structure of the underlying speech sound. In this work, we derive a statistical estimator that explicitly takes into account information about the characteristic structure of voiced speech by means of a harmonic signal model. To this end, we also present a way to estimate a harmonic model-based clean speech representation and the corresponding error variance directly in the short-time Fourier transform domain. The resulting estimator is optimal in the minimum-mean-squared error sense and can conveniently be formulated in terms of a multichannel Wiener filter. The proposed estimator outperforms several reference algorithms in terms of speech quality and intelligibility as predicted by instrumental measures.