  In this paper, we study several microphone channel selection and weighting
methods for robust automatic speech recognition (ASR) in noisy conditions. For
channel selection, we investigate two methods based on the maximum likelihood
(ML) criterion and minimum autoencoder reconstruction criterion, respectively.
For channel weighting, we produce enhanced log Mel filterbank coefficients as a
weighted sum of the coefficients of all channels. The weights of the channels
are estimated by using the ML criterion with constraints. We evaluate the
proposed methods on the CHiME-3 noisy ASR task. Experiments show that channel
weighting significantly outperforms channel selection due to its higher
flexibility. Furthermore, on real test data in which different channels have
different gains of the target signal, the channel weighting method performs
equally well or better than the MVDR beamforming, despite the fact that the
channel weighting does not make use of the phase delay information which is
normally used in beamforming.
