  Current front-ends for robust automatic speech recognition(ASR) include
masking- and mapping-based deep learning approaches to speech enhancement. A
recently proposed deep learning approach toa prioriSNR estimation, called
DeepXi, was able to produce enhanced speech at a higher quality and
intelligibility than current masking- and mapping-based approaches. Motivated
by this, we investigate Deep Xi as a front-end for robust ASR. Deep Xi is
evaluated using real-world non-stationary and coloured noise sources at
multiple SNR levels. Our experimental investigation shows that DeepXi as a
front-end is able to produce a lower word error rate than recent masking- and
mapping-based deep learning front-ends. The results presented in this work show
that Deep Xi is a viable front-end, and is able to significantly increase the
robustness of an ASR system. Availability: Deep Xi is available
at:https://github.com/anicolson/DeepXi
