MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech Recognition
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 Learning dataset designed for controlled analysis of linguistic content, speaker characteristics, and acoustic environments. Furthermore, we propose a real-world-inspired continual learning curriculum to simulate incremental updates and study how robustness is acquired, transferred, and forgotten. We evaluate three continual learning strategies and provide detailed insights into robustness under evolving conditions.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents MoDiCoL, a Modular Diagnostic Continual Learning dataset aimed at improving ASR robustness to distribution shifts, providing a comprehensive methodology and experimental evaluation that contributes significantly to the field of machine learning and speech recognition.
Methodology
The paper introduces a novel dataset, MoDiCoL, designed to evaluate ASR robustness under various real-world conditions through a modular and factorial design. The methodology is well-structured, incorporating a continual learning curriculum that simulates real-world distribution shifts. The use of synthetic and real speech data, combined with a comprehensive augmentation pipeline, enhances the dataset's applicability. The continual learning strategies evaluated (Experience Replay, Representation-level Regularization, and Orthogonal Gradient Descent) provide a robust framework for analyzing model performance across different drift types, showcasing a thoughtful approach to addressing the challenges of ASR robustness.
Results & evaluation
The experiments are thorough, with a clear evaluation of the dataset's quality and the performance of different continual learning methods. The metrics used, including Average Word Error Rate (A-WER) and Forgetting Measure (FM), are appropriate for assessing the effectiveness of the proposed methods. The results demonstrate significant insights into how ASR models adapt to various drift conditions, with detailed analysis of the performance across different tasks. The comparative analysis of CL methods adds depth to the findings, although more extensive experiments could further validate the robustness of the conclusions.
Reproducibility
The paper provides sufficient detail regarding the dataset construction, augmentation methods, and experimental setup, which supports reproducibility. The availability of the dataset on Hugging Face is a positive aspect, allowing other researchers to replicate the study. However, the exact implementation details of the continual learning strategies could be better documented to enhance reproducibility.
Limitations
One limitation is the reliance on synthetic speech generation, which may not fully capture the nuances of real-world speech variability. Additionally, while the dataset is comprehensive, it may still miss certain edge cases or combinations of factors that occur in real-world scenarios. The paper could also benefit from a more extensive discussion on the limitations of the continual learning methods used and their applicability to different ASR systems.
Broader impact
The work has significant implications for the development of more robust ASR systems, particularly in real-world applications where variability is common. By addressing the shortcomings of existing datasets and benchmarks, this research could lead to improved ASR performance across diverse populations and environments, ultimately enhancing accessibility and usability in various domains such as healthcare, education, and customer service. The paper presents MoDiCoL, a Modular Diagnostic Continual Learning dataset aimed at improving ASR robustness to distribution shifts, providing a comprehensive methodology and experimental evaluation that contributes significantly to the field of machine learning and speech recognition.
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