  Automatic Speech Recognition (ASR) systems suffer considerably when source
speech is corrupted with noise or room impulse responses (RIR). Typically,
speech enhancement is applied in both mismatched and matched scenario training
and testing. In matched setting, acoustic model (AM) is trained on
dereverberated far-field features while in mismatched setting, AM is fixed. In
recent past, mapping speech features from far-field to close-talk using
denoising autoencoder (DA) has been explored. In this paper, we focus on
matched scenario training and show that the proposed joint VAE based mapping
achieves a significant improvement over DA. Specifically, we observe an
absolute improvement of 2.5% in word error rate (WER) compared to DA based
enhancement and 3.96% compared to AM trained directly on far-field filterbank
features.
