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In this paper, we study several microphone channel selection and weightingmethods for robust automatic speech recognition (ASR) in noisy conditions. Forchannel 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 aweighted sum of the coefficients of all channels. The weights of the channelsare estimated by using the ML criterion with constraints. We evaluate theproposed methods on the CHiME-3 noisy ASR task. Experiments show that channelweighting significantly outperforms channel selection due to its higherflexibility. Furthermore, on real test data in which different channels havedifferent gains of the target signal, the channel weighting method performsequally well or better than the MVDR beamforming, despite the fact that thechannel weighting does not make use of the phase delay information which isnormally used in beamforming.

Noise Robust Speech Recognition Using Multi-Channel Based Channel Selection And ChannelWeighting