Deep Xi as a Front-End for Robust Automatic Speech Recognition
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
Cite
@article{arxiv.1906.07319,
title = {Deep Xi as a Front-End for Robust Automatic Speech Recognition},
author = {Aaron Nicolson and Kuldip K. Paliwal},
journal= {arXiv preprint arXiv:1906.07319},
year = {2020}
}