This paper investigates the combination of evidence coming from different frequency channels obtained filtering the speech signal at different auditory and modulation frequencies. In our previous work \cite{icassp2008}, we showed that combination of classifiers trained on different ranges of {\it modulation} frequencies is more effective if performed in sequential (hierarchical) fashion. In this work we verify that combination of classifiers trained on different ranges of {\it auditory} frequencies is more effective if performed in parallel fashion. Furthermore we propose an architecture based on neural networks for combining evidence coming from different auditory-modulation frequency sub-bands that takes advantages of previous findings. This reduces the final WER by 6.2% (from 45.8% to 39.6%) w.r.t the single classifier approach in a LVCSR task.