  In recent years, monaural speech separation has been formulated as a
supervised learning problem, which has been systematically researched and shown
the dramatical improvement of speech intelligibility and quality for human
listeners. However, it has not been well investigated whether the methods can
be employed as the front-end processing and directly improve the performance of
a machine listener, i.e., an automatic speech recognizer, without retraining or
joint-training the acoustic model. In this paper, we explore the effectiveness
of the independent front-end processing for the multi-conditional trained ASR
on the CHiME-3 challenge. We find that directly feeding the enhanced features
to ASR can make 36.40% and 11.78% relative WER reduction for the GMM-based and
DNN-based ASR respectively. We also investigate the affect of noisy phase and
generalization ability under unmatched noise condition.
