We present a new adaptive method for online noise estimation which extends the model combination approach to slowly varying noise conditions. The technique of model combination is reported to improve accuracy in speech recognition without extensive training of noisy speech data. Only training of noise characteristics is needed. However, if the noise characteristics vary over time, calculation of noise parameters once before recognition is not suitable. Therefore the new method of online estimation allows an adaptation to the current noise situation. Furthermore cepstral mean subtraction is added to the model combination scheme. This removes convolutional noise as well. Finally, it is shown how linear discriminant analysis eases handling of dynamical effects for model combination.