  We propose a novel method for generating scene-aware training data for
far-field automatic speech recognition. We use a deep learning-based estimator
to non-intrusively compute the sub-band reverberation time of an environment
from its speech samples. We model the acoustic characteristics of a scene with
its reverberation time and represent it using a multivariate Gaussian
distribution. We use this distribution to select acoustic impulse responses
from a large real-world dataset for augmenting speech data. The speech
recognition system trained on our scene-aware data consistently outperforms the
system trained using many more random acoustic impulse responses on the REVERB
and the AMI far-field benchmarks. In practice, we obtain 2.64% absolute
improvement in word error rate compared with using training data of the same
size with uniformly distributed reverberation times.
