# An Investigation of End-to-End Models for Robust Speech Recognition

arXiv: https://arxiv.org/abs/2102.06237 · HTML: https://mlsanity.com/paper/2102.06237/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [SEGAN: Speech Enhancement Generative Adversarial Network](https://mlsanity.com/paper/1703.09452/) (2017)
- [Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments](https://mlsanity.com/paper/1705.10874/) (2017)
- [Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation](https://mlsanity.com/paper/1707.06265/) (2017)
- [Learning Noise-Invariant Representations for Robust Speech Recognition](https://mlsanity.com/paper/1807.06610/) (2018)
- [Very Deep Convolutional Neural Networks for Robust Speech Recognition](https://mlsanity.com/paper/1610.00277/) (2016)
- [Deep Xi as a Front-End for Robust Automatic Speech Recognition](https://mlsanity.com/paper/1906.07319/) (2019)

## Built on by (most cited first, 9 of 9)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data](https://mlsanity.com/paper/2209.15329/) (2022)
- [Large Language Models are Efficient Learners of Noise-Robust Speech Recognition](https://mlsanity.com/paper/2401.10446/) (2024)
- [Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR](https://mlsanity.com/paper/2304.04974/) (2023)
- [Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models](https://mlsanity.com/paper/2405.14161/) (2024)
- [D4AM: A General Denoising Framework for Downstream Acoustic Models](https://mlsanity.com/paper/2311.16595/) (2023)
- [LipGER: Visually-Conditioned Generative Error Correction for Robust Automatic Speech Recognition](https://mlsanity.com/paper/2406.04432/) (2024)
- [When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Systems](https://mlsanity.com/paper/2512.17562/) (2025)
- [Pretrained ASR Pseudo-labeling for Noisy Police Audio](https://mlsanity.com/paper/2609.30469/) (2026)

## Abstract (arXiv)

End-to-end models for robust automatic speech recognition (ASR) have not been sufficiently well-explored in prior work. With end-to-end models, one could choose to preprocess the input speech using speech enhancement techniques and train the model using enhanced speech. Another alternative is to pass the noisy speech as input and modify the model architecture to adapt to noisy speech. A systematic comparison of these two approaches for end-to-end robust ASR has not been attempted before. We address this gap and present a detailed comparison of speech enhancement-based techniques and three different model-based adaptation techniques covering data augmentation, multi-task learning, and adversarial learning for robust ASR. While adversarial learning is the best-performing technique on certain noise types, it comes at the cost of degrading clean speech WER. On other relatively stationary noise types, a new speech enhancement technique outperformed all the model-based adaptation techniques. This suggests that knowledge of the underlying noise type can meaningfully inform the choice of adaptation technique.