  Distant speech recognition is a challenge, particularly due to the corruption
of speech signals by reverberation caused by large distances between the
speaker and microphone. In order to cope with a wide range of reverberations in
real-world situations, we present novel approaches for acoustic modeling
including an ensemble of deep neural networks (DNNs) and an ensemble of jointly
trained DNNs. First, multiple DNNs are established, each of which corresponds
to a different reverberation time 60 (RT60) in a setup step. Also, each model
in the ensemble of DNN acoustic models is further jointly trained, including
both feature mapping and acoustic modeling, where the feature mapping is
designed for the dereverberation as a front-end. In a testing phase, the two
most likely DNNs are chosen from the DNN ensemble using maximum a posteriori
(MAP) probabilities, computed in an online fashion by using maximum likelihood
(ML)-based blind RT60 estimation and then the posterior probability outputs
from two DNNs are combined using the ML-based weights as a simple average.
Extensive experiments demonstrate that the proposed approach leads to
substantial improvements in speech recognition accuracy over the conventional
DNN baseline systems under diverse reverberant conditions.
