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arXiv · 1906.07319

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

Aaron Nicolson·Kuldip K. Paliwal
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Abstract

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

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BibTeXRIS

Aaron Nicolson, Kuldip K. Paliwal. 2019-06-18. Deep Xi as a Front-End for Robust Automatic Speech Recognition. https://arxiv.org/abs/1906.07319

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