  Speech recognition in adverse real-world environments is highly affected by
reverberation and nonstationary background noise. A well-known strategy to
reduce such undesired signal components in multi-microphone scenarios is
spatial filtering of the microphone signals. In this article, we demonstrate
that an additional coherence-based postfilter, which is applied to the
beamformer output signal to remove diffuse interference components from the
latter, is an effective means to further improve the recognition accuracy of
modern deep learning speech recognition systems. To this end, the recently
updated 3rd CHiME Speech Separation and Recognition Challenge (CHiME-3)
baseline speech recognition system is extended by a coherence-based postfilter
and the postfilter's impact on the word error rates is investigated for the
noisy environments provided by CHiME-3. To determine the time- and
frequency-dependent postfilter gains, we use a Direction-of-Arrival
(DOA)-dependent and a DOA-independent estimator of the coherent-to-diffuse
power ratio as an approximation of the short-time signal-to-noise ratio. Our
experiments show that incorporating coherence-based postfiltering into the
CHiME-3 baseline speech recognition system leads to a significant reduction of
the word error rate scores for the noisy and reverberant environments provided
as part of CHiME-3.
