  Despite the significant progress made in the last years, state-of-the-art
speech recognition technologies provide a satisfactory performance only in the
close-talking condition. Robustness of distant speech recognition in adverse
acoustic conditions, on the other hand, remains a crucial open issue for future
applications of human-machine interaction. To this end, several advances in
speech enhancement, acoustic scene analysis as well as acoustic modeling, have
recently contributed to improve the state-of-the-art in the field. One of the
most effective approaches to derive a robust acoustic modeling is based on
using contaminated speech, which proved helpful in reducing the acoustic
mismatch between training and testing conditions.
  In this paper, we revise this classical approach in the context of modern
DNN-HMM systems, and propose the adoption of three methods, namely, asymmetric
context windowing, close-talk based supervision, and close-talk based
pre-training. The experimental results, obtained using both real and simulated
data, show a significant advantage in using these three methods, overall
providing a 15% error rate reduction compared to the baseline systems. The same
trend in performance is confirmed either using a high-quality training set of
small size, and a large one.
