  Reverberation and additive noise have detrimental effects on the performance
of automatic speech recognition systems. In this paper we explore the ability
of a DNN-based spectral feature mapping to remove the effects of reverberation
and additive noise. Experiments with the CHiME-2 database show that this DNN
can achieve an average reduction in WER of 4.5%, when compared to the baseline
system, at SNRs equal to -6 dB, -3 dB, 0 dB and 3 dB, and just 0.8% at greater
SNRs of 6 dB and 9 dB. These results suggest that this DNN is more effective in
removing additive noise than reverberation. To improve the DNN performance, we
combine it with the weighted prediction error (WPE) method that shows a
complementary behavior. While this combination provided a reduction in WER of
approximately 11% when compared with the baseline, the observed improvement is
not as great as that obtained using WPE alone. However, modifications to the
DNN training process were applied and an average reduction in WER equal to
18.3% was achieved when compared with the baseline system. Furthermore, the
improved DNN combined with WPE achieves a reduction in WER of 7.9% when
compared with WPE alone.
