  Integration of multiple microphone data is one of the key ways to achieve
robust speech recognition in noisy environments or when the speaker is located
at some distance from the input device. Signal processing techniques such as
beamforming are widely used to extract a speech signal of interest from
background noise. These techniques, however, are highly dependent on prior
spatial information about the microphones and the environment in which the
system is being used. In this work, we present a neural attention network that
directly combines multi-channel audio to generate phonetic states without
requiring any prior knowledge of the microphone layout or any explicit signal
preprocessing for speech enhancement. We embed an attention mechanism within a
Recurrent Neural Network (RNN) based acoustic model to automatically tune its
attention to a more reliable input source. Unlike traditional multi-channel
preprocessing, our system can be optimized towards the desired output in one
step. Although attention-based models have recently achieved impressive results
on sequence-to-sequence learning, no attention mechanisms have previously been
applied to learn potentially asynchronous and non-stationary multiple inputs.
We evaluate our neural attention model on the CHiME-3 challenge task, and show
that the model achieves comparable performance to beamforming using a purely
data-driven method.
