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Current front-ends for robust automatic speech recognition(ASR) includemasking- and mapping-based deep learning approaches to speech enhancement. Arecently proposed deep learning approach toa prioriSNR estimation, calledDeepXi, was able to produce enhanced speech at a higher quality andintelligibility than current masking- and mapping-based approaches. Motivatedby this, we investigate Deep Xi as a front-end for robust ASR. Deep Xi isevaluated using real-world non-stationary and coloured noise sources atmultiple SNR levels. Our experimental investigation shows that DeepXi as afront-end is able to produce a lower word error rate than recent masking- andmapping-based deep learning front-ends. The results presented in this work showthat Deep Xi is a viable front-end, and is able to significantly increase therobustness of an ASR system. Availability: Deep Xi is availableat:https://github.com/anicolson/DeepXi

Deep Xi as a Front-End for Robust Automatic Speech Recognition