Generative Testing of Automated Speech Recognition Systems
Automatic speech recognition (ASR) systems have achieved high accuracy with transformer-based models, enabling deployment in critical applications. However, they remain vulnerable to adversarial manipulation, particularly in black-box settings where attacks must preserve perceptual naturalness. This work introduces GATAS, a black-box testing approach that generates failure inducing inputs by operating in the phoneme-level latent space of a text- to-speech model. Instead of perturbing waveforms directly, the approach interpolates latent representations to induce transcription errors while remaining within the manifold of natural speech. The attack is formulated as a multi-objective optimiz...
| Authors | Yanis Xabier Wilbrand Peña, Oliver Weißl, Andrea Stocco |
|---|---|
| Primary category | cs.CR |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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