Environmental adaptation based on first order approximation

We propose an algorithm that compensates for both additive and convolutional noise. The goal of this method is to achieve an efficient environmental adaptation to realistic environments both in terms of computation time and memory. The algorithm described in this paper is an extension of an additive noise adaptation algorithm. Experimental results are given on a realistic database recorded in a car. This database is further filtered by a low pass filter to combine additive and channel noise. The proposed adaptation algorithm reduces the error rate by 75 % on this database, when compared to our baseline system without environmental adaptation.