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

arXiv: https://arxiv.org/abs/1906.07319 · HTML: https://mlsanity.com/paper/1906.07319/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [An Investigation of End-to-End Models for Robust Speech Recognition](https://mlsanity.com/paper/2102.06237/) (2021)
- [NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling](https://mlsanity.com/paper/2206.09058/) (2022)
- [Training-Free Intelligibility-Guided Observation Addition for Noisy ASR](https://mlsanity.com/paper/2602.20967/) (2026)

## Abstract (arXiv)

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