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.
Bibliographic reference. Ravanelli, Mirco / Omologo, Maurizio (2015): "Contaminated speech training methods for robust DNN-HMM distant speech recognition", In INTERSPEECH-2015, 756-760.