# Language Dependencies in Adversarial Attacks on Speech Recognition Systems

arXiv: https://arxiv.org/abs/2202.00399 · HTML: https://mlsanity.com/paper/2202.00399/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [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)
- [SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems](https://mlsanity.com/paper/2007.06622/) (2020)
- [Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems](https://mlsanity.com/paper/1908.01551/) (2019)
- [LeakyPick: IoT Audio Spy Detector](https://mlsanity.com/paper/2007.00500/) (2020)

## Built on by (most cited first, 1 of 1)

- [There is more than one kind of robustness: Fooling Whisper with adversarial examples](https://mlsanity.com/paper/2210.17316/) (2022)

## Abstract (arXiv)

Automatic speech recognition (ASR) systems are ubiquitously present in our daily devices. They are vulnerable to adversarial attacks, where manipulated input samples fool the ASR system's recognition. While adversarial examples for various English ASR systems have already been analyzed, there exists no inter-language comparative vulnerability analysis. We compare the attackability of a German and an English ASR system, taking Deepspeech as an example. We investigate if one of the language models is more susceptible to manipulations than the other. The results of our experiments suggest statistically significant differences between English and German in terms of computational effort necessary for the successful generation of adversarial examples. This result encourages further research in language-dependent characteristics in the robustness analysis of ASR.