In prior work, the current authors investigated beamforming algorithms that exploit the non-Gaussianity of human speech. The beamformers we proposed were designed to maximize the kurtosis or negentropy of the subband output subject to the distortionless constraint for the direction of interest. Such techniques are able to suppress interference signals as well as reverberation effects without signal cancellation. However, multiple passes of processing were required for each utterance in order to estimate the active weight vector. Hence, they were unsuitable for online implementation. In this work, we propose an online implementation of the maximum kurtosis beamformer. In a set of distant speech recognition experiments on far-field data, we demonstrate the effectiveness of the proposed technique. Compared to a single channel of the array, the proposed algorithm reduced word error rate from 15.4% to 6.5%.

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