  Far-field speech recognition in noisy and reverberant conditions remains a
challenging problem despite recent deep learning breakthroughs. This problem is
commonly addressed by acquiring a speech signal from multiple microphones and
performing beamforming over them. In this paper, we propose to use a recurrent
neural network with long short-term memory (LSTM) architecture to adaptively
estimate real-time beamforming filter coefficients to cope with non-stationary
environmental noise and dynamic nature of source and microphones positions
which results in a set of timevarying room impulse responses. The LSTM adaptive
beamformer is jointly trained with a deep LSTM acoustic model to predict senone
labels. Further, we use hidden units in the deep LSTM acoustic model to assist
in predicting the beamforming filter coefficients. The proposed system achieves
7.97% absolute gain over baseline systems with no beamforming on CHiME-3 real
evaluation set.
