# Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey

arXiv: https://arxiv.org/abs/2202.10594 · HTML: https://mlsanity.com/paper/2202.10594/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 94 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition](https://mlsanity.com/paper/1903.10346/) (2019)
- [Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling](https://mlsanity.com/paper/1902.08295/) (2019)
- [DolphinAtack: Inaudible Voice Commands](https://mlsanity.com/paper/1708.09537/) (2017)
- [End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results](https://mlsanity.com/paper/1412.1602/) (2014)
- [Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective](https://mlsanity.com/paper/2009.03728/) (2020)
- [Large-Scale Visual Speech Recognition](https://mlsanity.com/paper/1807.05162/) (2018)
- [Deep Multimodal Semantic Embeddings for Speech and Images](https://mlsanity.com/paper/1511.03690/) (2015)
- [Efficient Adversarial Training with Transferable Adversarial Examples](https://mlsanity.com/paper/1912.11969/) (2019)
- [Houdini: Fooling Deep Structured Prediction Models](https://mlsanity.com/paper/1707.05373/) (2017)
- [Did you hear that? Adversarial Examples Against Automatic Speech Recognition](https://mlsanity.com/paper/1801.00554/) (2018)
- [Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction](https://mlsanity.com/paper/1911.11616/) (2019)
- [Targeted Adversarial Examples for Black Box Audio Systems](https://mlsanity.com/paper/1805.07820/) (2018)
- [English Conversational Telephone Speech Recognition by Humans and Machines](https://mlsanity.com/paper/1703.02136/) (2017)

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

- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [Identifying Adversarially Attackable and Robust Samples](https://mlsanity.com/paper/2301.12896/) (2023)
- [Towards stable AI systems for Evaluating Arabic Pronunciations](https://mlsanity.com/paper/2508.19587/) (2025)

## Abstract (arXiv)

A Machine-Critical Application is a system that is fundamentally necessary to the success of specific and sensitive operations such as search and recovery, rescue, military, and emergency management actions. Recent advances in Machine Learning, Natural Language Processing, voice recognition, and speech processing technologies have naturally allowed the development and deployment of speech-based conversational interfaces to interact with various machine-critical applications. While these conversational interfaces have allowed users to give voice commands to carry out strategic and critical activities, their robustness to adversarial attacks remains uncertain and unclear. Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications. The most common reason of adversarial attacks is to cause a malfunction in a machine learning model. An adversarial attack might entail presenting a model with inaccurate or fabricated samples as it's training data, or introducing maliciously designed data to deceive an already trained model. While focusing on speech recognition for machine-critical applications, in this paper, we first review existing speech recognition techniques, then, we investigate the effectiveness of adversarial attacks and defenses against these systems, before outlining research challenges, defense recommendations, and future work. This paper is expected to serve researchers and practitioners as a reference to help them in understanding the challenges, position themselves and, ultimately, help them to improve existing models of speech recognition for mission-critical applications. Keywords: Mission-Critical Applications, Adversarial AI, Speech Recognition Systems.