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

## TL;DR

This paper investigates Deep Xi, a deep learning approach for speech enhancement, as a front-end for robust automatic speech recognition, demonstrating it reduces word error rates more effectively than existing methods in noisy conditions.

## Contribution

The study evaluates Deep Xi as a front-end for ASR, showing it outperforms current masking- and mapping-based approaches in noisy environments.

## Key findings

- Deep Xi achieves lower word error rates than recent methods.
- Deep Xi enhances speech quality and intelligibility.
- It significantly improves ASR robustness in real-world noise.

## 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