  For a speech-enhancement algorithm, it is highly desirable to simultaneously
improve perceptual quality and recognition rate. Thanks to computational costs
and model complexities, it is challenging to train a model that effectively
optimizes both metrics at the same time. In this paper, we propose a method for
speech enhancement that combines local and global contextual structures
information through convolutional-recurrent neural networks that improves
perceptual quality. At the same time, we introduce a new constraint on the
objective function using a language model/decoder that limits the impact on
recognition rate. Based on experiments conducted with real user data, we
demonstrate that our new context-augmented machine-learning approach for speech
enhancement improves PESQ and WER by an additional 24.5% and 51.3%,
respectively, when compared to the best-performing methods in the literature.
