Are ASR foundation models generalized enough to capture features of regional dialects for low-resource languages?
This study investigates the generalization capability of speech foundation models for automatic speech recognition (ASR) in low-resource regional dialects. To address the lack of benchmark resources for Bangla dialect ASR, we construct and publicly release Ben-10—the first high-quality dialectal speech dataset for Bangla (78 hours, covering 10 regional variants). Experiments reveal that state-of-the-art foundation models suffer substantial performance degradation—both in zero-shot and fine-tuned settings—on out-of-distribution (OOD) dialects, exposing critical robustness limitations. In contrast, dialect-specific modeling significantly improves accuracy. Methodologically, we introduce a linguistics-informed data curation pipeline and an OOD evaluation framework tailored to dialectal variation. Our work is the first to systematically characterize the limitations of foundation models in low-resource dialect ASR. We contribute Ben-10, open-source code, and strong baselines, establishing a new benchmark for dialect-aware ASR research.