An evaluation study on speech feature densities for Bayesian estimation in robust ASR
Proceedings of the Third COST 2102 international training school conference on Toward autonomous, adaptive, and context-aware multimodal interfaces: theoretical and practical issues
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This paper presents a new class of estimators for speech enhancement in the discrete Fourier transform (DFT) domain, where we consider a multidimensional normal inverse Gaussian (MNIG) distribution for the speech DFT coefficients. The MNIG distribution can model a wide range of processes, from heavy-tailed to less heavy-tailed processes. Under the MNIG distribution complex DFT and amplitude estimators are derived. In contrast to other estimators, the suppression characteristics of the MNIG-based estimators can be adapted online to the underlying distribution of the speech DFT coefficients. Compared to noise suppression algorithms based on preselected super-Gaussian distributions, the MNIG-based complex DFT and amplitude estimators lead to a performance improvement in terms of segmental signal-to-noise ratio (SNR) in the order of 0.3 to 0.6 dB and 0.2 to 0.6 dB, respectively