MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech Recognition
Researchers have introduced MoDiCoL, a new dataset designed to improve the robustness of Automatic Speech Recognition (ASR) systems. Unlike existing datasets that isolate factors like accents or noise, MoDiCoL allows for the controlled analysis of linguistic content, speaker characteristics, and acoustic environments, including their co-occurrence. The dataset is paired with a continual learning curriculum to simulate real-world incremental updates and study how ASR models acquire, transfer, and forget robustness under evolving conditions. AI
IMPACT This dataset aims to bridge the gap between ASR performance on benchmarks and real-world applications by addressing distribution shifts.