  We present a method for improving the quality of synthetic room impulse
responses for far-field speech recognition. We bridge the gap between the
fidelity of synthetic room impulse responses (RIRs) and the real room impulse
responses using our novel, TS-RIRGAN architecture. Given a synthetic RIR in the
form of raw audio, we use TS-RIRGAN to translate it into a real RIR. We also
perform real-world sub-band room equalization on the translated synthetic RIR.
Our overall approach improves the quality of synthetic RIRs by compensating
low-frequency wave effects, similar to those in real RIRs. We evaluate the
performance of improved synthetic RIRs on a far-field speech dataset augmented
by convolving the LibriSpeech clean speech dataset [1] with RIRs and adding
background noise. We show that far-field speech augmented using our improved
synthetic RIRs reduces the word error rate by up to 19.9% in Kaldi far-field
automatic speech recognition benchmark [2].
