HMM-based estimation of unreliable spectral components for noise robust speech recognition
Bengt Jonas Borgstrom, Abeer A. Alwan · 2008
This paper presents a novel approach for reconstructing unreliable spectral components, which utilizes HMM-based missing feature algorithms, and applies them to noise robust speech recognition. The proposed technique uses the forwardbackward algorithm to estimate corrupt spectrographic data based on nearby reliable features, noisy observations, and on an underlying statistical model. The estimation process can be applied based on intra-channel information, intra-feature information, or a combination of both. The overall system is shown to provide vast improvements for the Consonant Challenge Database [1], for both MFCCs and PLP features, when using an oracle mask. Moreover, through downsampling of statistical models [2], the required complexity of the system is greatly reduced with negligible effects on results.