Generative Testing of Automated Speech Recognition Systems

• URL: http://arxiv.org/abs/2607.09833v1
• Date: Fri, 10 Jul 2026 14:04:28 GMT
• Title: Generative Testing of Automated Speech Recognition Systems
• Abstract summary: We introduce GATAS, a black-box testing approach that generates failure inducing inputs.<n>Instead of perturbing waveforms directly, the approach interpolates latent representations to induce transcription errors.<n>We show that GATAS achieves a 98% success rate while producing lower distortion and higher perceptual quality.
• Score: 0.4914401344126589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 optimization problem balancing semantic divergence and perceptual quality. Our empirical evaluation against both white-box and black-box baselines shows that GATAS achieves a 98% success rate while producing lower distortion and higher perceptual quality, as confirmed by human studies. Despite operating without gradient access, GATAS remains competitive against white-box methods, highlighting that representation and perceptual alignment are more critical than access to model internals. Overall, our results demonstrate that untargeted latent-space optimization enables the efficient generation of realistic and effective test cases for ASR systems.

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