# Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

arXiv: https://arxiv.org/abs/1705.10874 · HTML: https://mlsanity.com/paper/1705.10874/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2017.

## Built on by (most cited first, 30 of 30)

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- [Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes](https://mlsanity.com/paper/2412.13573/) (2024)
- [An Investigation of End-to-End Models for Robust Speech Recognition](https://mlsanity.com/paper/2102.06237/) (2021)
- [Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies](https://mlsanity.com/paper/2603.16952/) (2026)
- [Automated Speech Scoring System Under The Lens: Evaluating and interpreting the linguistic cues for language proficiency](https://mlsanity.com/paper/2111.15156/) (2021)
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- [Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries](https://mlsanity.com/paper/2105.09930/) (2021)
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- [Blind Acoustic Room Parameter Estimation Using Phase Features](https://mlsanity.com/paper/2303.07449/) (2023)
- [MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles](https://mlsanity.com/paper/2109.12838/) (2021)
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- [Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps](https://mlsanity.com/paper/2305.03963/) (2023)
- [XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications](https://mlsanity.com/paper/2312.06082/) (2023)
- [Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View Data](https://mlsanity.com/paper/2403.10663/) (2024)
- [The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Input](https://mlsanity.com/paper/2108.01245/) (2021)
- [On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training](https://mlsanity.com/paper/2205.01751/) (2022)
- [Rethinking skip connection model as a learnable Markov chain](https://mlsanity.com/paper/2209.15278/) (2022)
- [Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language Models](https://mlsanity.com/paper/2407.12094/) (2024)
- [ICo3D: An Interactive Conversational 3D Virtual Human](https://mlsanity.com/paper/2601.13148/) (2026)
- [Unsupervised domain adaptation via double classifiers based on high confidence pseudo label](https://mlsanity.com/paper/2105.04729/) (2021)
- [Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space](https://mlsanity.com/paper/2408.05917/) (2024)
- [A Framework to Enable Algorithmic Design Choice Exploration in DNNs](https://mlsanity.com/paper/2410.08300/) (2024)
- [Degrading Voice: A Comprehensive Overview of Robust Voice Conversion Through Input Manipulation](https://mlsanity.com/paper/2512.06304/) (2025)
- [Retina gap junctions support the robust perception by warping neural representational geometries along the visual hierarchy](https://mlsanity.com/paper/2604.14200/) (2026)
- [Optimal transport meets speech: a tutorial review](https://mlsanity.com/paper/2609.31787/) (2026)

## Abstract (arXiv)

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.