# Crafting Adversarial Examples For Speech Paralinguistics Applications

arXiv: https://arxiv.org/abs/1711.03280 · HTML: https://mlsanity.com/paper/1711.03280/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2017.

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

- [Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information](https://mlsanity.com/paper/2110.09714/) (2021)
- [Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition](https://mlsanity.com/paper/2206.03393/) (2022)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models](https://mlsanity.com/paper/2405.06134/) (2024)
- [AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems](https://mlsanity.com/paper/2206.03351/) (2022)
- [QFA2SR: Query-Free Adversarial Transfer Attacks to Speaker Recognition Systems](https://mlsanity.com/paper/2305.14097/) (2023)
- [MASTERKEY: Practical Backdoor Attack Against Speaker Verification Systems](https://mlsanity.com/paper/2309.06981/) (2023)
- ["I am bad": Interpreting Stealthy, Universal and Robust Audio Jailbreaks in Audio-Language Models](https://mlsanity.com/paper/2502.00718/) (2025)
- [Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models](https://mlsanity.com/paper/2411.14842/) (2024)
- [Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems](https://mlsanity.com/paper/2105.10707/) (2021)
- [Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition](https://mlsanity.com/paper/2203.04696/) (2022)
- [Universal Acoustic Adversarial Attacks for Flexible Control of Speech-LLMs](https://mlsanity.com/paper/2505.14286/) (2025)
- [SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems](https://mlsanity.com/paper/2103.10651/) (2021)
- [SEC4SR: A Security Analysis Platform for Speaker Recognition](https://mlsanity.com/paper/2109.01766/) (2021)
- [Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey](https://mlsanity.com/paper/2202.10594/) (2022)
- [Arabic Synonym BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2402.03477/) (2024)
- [Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generation](https://mlsanity.com/paper/2506.10685/) (2025)
- [Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Models](https://mlsanity.com/paper/2407.04482/) (2024)
- [Safeguarding Privacy in Edge Speech Understanding with Tiny Foundation Models](https://mlsanity.com/paper/2502.01649/) (2025)
- [Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems](https://mlsanity.com/paper/2203.05408/) (2022)
- [Disappeared Command: Spoofing Attack On Automatic Speech Recognition Systems with Sound Masking](https://mlsanity.com/paper/2204.08977/) (2022)
- [Adversarial Attacks on ASR Systems: An Overview](https://mlsanity.com/paper/2208.02250/) (2022)
- [Model-Agnostic Reachability Analysis on Deep Neural Networks](https://mlsanity.com/paper/2304.00813/) (2023)
- [Generating Watermarked Adversarial Texts](https://mlsanity.com/paper/2110.12948/) (2021)
- [Automating the Design and Development of Gradient Descent Trained Expert System Networks](https://mlsanity.com/paper/2207.02845/) (2022)
- [Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition](https://mlsanity.com/paper/2302.08973/) (2023)
- [Beyond Neural-on-Neural Approaches to Speaker Gender Protection](https://mlsanity.com/paper/2306.17700/) (2023)
- [Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation](https://mlsanity.com/paper/2309.03238/) (2023)
- [Are Deep Speech Denoising Models Robust to Adversarial Noise?](https://mlsanity.com/paper/2503.11627/) (2025)

## Abstract (arXiv)

Computational paralinguistic analysis is increasingly being used in a wide range of cyber applications, including security-sensitive applications such as speaker verification, deceptive speech detection, and medical diagnostics. While state-of-the-art machine learning techniques, such as deep neural networks, can provide robust and accurate speech analysis, they are susceptible to adversarial attacks. In this work, we propose an end-to-end scheme to generate adversarial examples for computational paralinguistic applications by perturbing directly the raw waveform of an audio recording rather than specific acoustic features. Our experiments show that the proposed adversarial perturbation can lead to a significant performance drop of state-of-the-art deep neural networks, while only minimally impairing the audio quality.