arXiv:2606.14459v1 Announce Type: cross Abstract: Modern Automatic Speech Recognition (ASR) systems have made remarkable progress on standard benchmarks, yet performance gaps have emerged under real-world distribution shifts, caused by recording conditions, accents, speech impairments, and noise. Existing datasets and benchmarks typically isolate these factors, which overlooks their co-occurrence in real-world applications. In this paper, we argue that model robustness can be treated as a dynamic capability that continually develops, and we introduce MoDiCoL, a Modular Diagnostic Continual Lea
The proliferation of real-world ASR applications highlights limitations of current models under diverse, co-occurring conditions, prompting new diagnostic datasets.
This development addresses a fundamental challenge for advanced AI: building robust systems that perform well in complex, unpredictable environments, critical for widespread adoption.
The focus shifts from isolated problem domains to integrated, modular diagnostics for AI robustness, enabling more adaptable and reliable AI systems.
- · AI researchers
- · ASR developers
- · Speech recognition users
- · Edge AI providers
- · Companies relying on non-robust ASR
- · AI models without continuous learning capabilities
Improved performance of Automatic Speech Recognition systems in real-world scenarios.
Accelerated development of AI agent systems capable of adapting to dynamic environmental inputs.
Enhanced trust and broader adoption of AI in critical applications where robustness and reliability are paramount.
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Read at arXiv cs.AI