Spectral tilt modelling with extrapolated GMMs for intelligibility enhancement of narrowband telephone speech

Post-processing methods are used in mobile communications to improve the intelligibility of speech in adverse background noise conditions. In this study, post-processing based on the modification of the spectral tilt with Gaussian mixture models according to the Lombard effect is investigated. A spectral envelope estimation method is studied and optimized for this purpose. Furthermore, the extrapolation of the statistical mapping in a post-processing context is investigated. The proposed post-processing methods are compared to unprocessed speech and a reference method in subjective intelligibility and quality tests in different near-end noise conditions. The results indicate that one of the extrapolated methods achieved the same intelligibility as fixed high-pass filtering without degrading the quality of speech.