Cross-Modal Robustness Transfer (CMRT): Training Robust Speech Translation Models Using Adversarial Text
arXiv:2602.11933v2 Announce Type: replace Abstract: End-to-End Speech Translation (E2E-ST) has seen significant advancements, yet current models are primarily benchmarked on curated, "clean" datasets. This overlooks critical real-world challenges, such as morphological robustness to inflectional variations common in non-native or dialectal speech. In this work, we adapt a text-based adversarial attack targeting inflectional morphology to the speech domain and demonstrate that state-of-the-art E2E-ST models are highly vulnerable it. While adversarial training effectively mitigates such risks in
The proliferation of real-world speech data, especially non-native and dialectal forms, is exposing limitations of current AI models. This research highlights the immediate need for robust speech translation in complex linguistic environments.
A strategic reader should care because vulnerabilities in core AI capabilities like speech translation undermine critical applications in national security, global communication, and commercial services. Solving robustness is crucial for reliable AI deployment.
The focus for developing speech translation models is shifting from mere accuracy on clean datasets to an emphasis on morphological robustness and adversarial defense. This alters the benchmarks and development priorities for AI researchers.
- · AI robustness researchers
- · Speech translation model developers
- · Companies with diverse linguistic user bases
- · Defense and intelligence sectors
- · E2E-ST models lacking robustness techniques
- · Developers solely focused on clean dataset performance
Increased investment and research into adversarial training and robust model architectures will follow in speech AI.
Improved speech translation models will enable more reliable cross-border communication and intelligence gathering, particularly in linguistically diverse regions.
The broader implication is a push towards foundational AI models inherently designed for resilience against diverse real-world inputs, rather than patching weaknesses post-deployment.
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Read at arXiv cs.CL