  We propose a novel approach for blind room impulse response (RIR) estimation
systems in the context of a downstream application scenario, far-field
automatic speech recognition (ASR). We first draw the connection between
improved RIR estimation and improved ASR performance, as a means of evaluating
neural RIR estimators. We then propose a generative adversarial network (GAN)
based architecture that encodes RIR features from reverberant speech and
constructs an RIR from the encoded features, and uses a novel energy decay
relief loss to optimize for capturing energy-based properties of the input
reverberant speech. We show that our model outperforms the state-of-the-art
baselines on acoustic benchmarks (by 17\% on the energy decay relief and 22\%
on an early-reflection energy metric), as well as in an ASR evaluation task (by
6.9\% in word error rate).
