Model-combination-based acoustic mapping

We propose a method for compensating distortions in the speech signal caused by environment changes. The basic method concentrates on additive noise, but can be extended to address also channel and to some extent speaker changes. By combining compensation with adaptation techniques it leads to high error rate reductions for mobile speech applications. Thereby, it is more efficient than adapting the acoustic model of the recognizer and more powerful than simple noise reduction techniques