MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech Recognition

The paper introduces MoDiCoL, a Modular Diagnostic Continual Learning dataset aimed at enhancing the robustness of Automatic Speech Recognition (ASR) systems under real-world distribution shifts. It features a structured curriculum for continual learning that allows for the analysis of linguistic content, speaker characteristics, and acoustic environments, while evaluating various continual learning strategies to understand how robustness is developed and maintained. This dataset is significant for practitioners as it addresses the complexities of real-world conditions that traditional benchmarks fail to capture, enabling more resilient ASR model training.