In the submitted system to CHiME-5 challenge, we propose front-end enhancement of the beamformed array utterances to mitigate mismatch conditions between close-talking utterances and array utterances. Our initial experiments showed that an Acoustic Model trained by using only close-talking microphone utterances gave a superior performance than the baseline acoustic model when tested using close-talking utterances of the development set. Taking this cue, we explored the hypothesis that if array utterances are mapped to corresponding close-talking utterances, the system trained using only worn utterances will perform better. Towards this end, we trained a Time Delay Neural Network De-noising autoencoder (TDNN-DAE) using non-overlapping speech close-talking microphone utterances (targets) and their corresponding beamform utterances. However, the proposed system could not outperform the baseline.