  For multi-channel speech recognition, speech enhancement techniques such as
denoising or dereverberation are conventionally applied as a front-end
processor. Deep learning-based front-ends using such techniques require aligned
clean and noisy speech pairs which are generally obtained via data simulation.
Recently, several joint optimization techniques have been proposed to train the
front-end without parallel data within an end-to-end automatic speech
recognition (ASR) scheme. However, the ASR objective is sub-optimal and
insufficient for fully training the front-end, which still leaves room for
improvement. In this paper, we propose a novel approach which incorporates
flow-based density estimation for the robust front-end using non-parallel clean
and noisy speech. Experimental results on the CHiME-4 dataset show that the
proposed method outperforms the conventional techniques where the front-end is
trained only with ASR objective.
