Inverse-Hessian Regularization for Continual Learning in ASR
Abstract
Catastrophic forgetting remains a major challenge for continual learning (CL) in automatic speech recognition (ASR), where models must adapt to new domains without losing performance on previously learned conditions. Several CL methods have been proposed for ASR, and, recently, weight averaging - where models are averaged in a merging step after fine-tuning - has proven effective as a simple memory-free strategy. However, it is heuristic in nature and ignores the underlying loss landscapes of the tasks, hindering adaptability. In this work, we propose Inverse Hessian Regularization (IHR), a memory-free approach for CL in ASR that incorporates curvature information into the merging step. After fine-tuning on a new task, the adaptation is adjusted through a Kronecker-factored inverse Hessian approximation of the previous task, ensuring that the model moves primarily in directions less harmful to past performance, while keeping the method lightweight. We evaluate IHR on two CL benchmarks and show that it significantly outperforms state-of-the-art baselines, reducing forgetting while improving adaptability. Ablation studies and analyses further confirm its effectiveness.
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
This paper introduces Inverse Hessian Regularization (IHR), a novel memory-free method for continual learning in automatic speech recognition, which effectively reduces forgetting while maintaining strong adaptability to new tasks. The comprehensive evaluation of IHR demonstrates its potential to significantly advance the field of continual learning in ASR, addressing critical challenges faced by existing methods.
Methodology
The proposed Inverse Hessian Regularization (IHR) method innovatively incorporates curvature information into the continual learning process for ASR. By using a Kronecker-factored approximation of the inverse Hessian, the method effectively adjusts the model updates to minimize catastrophic forgetting while maintaining adaptability to new tasks. The lightweight nature of the approach, requiring only a single application of the inverse Hessian per task, enhances its practicality. The methodology is well-structured and addresses key limitations of existing methods, such as weight averaging.
Results & evaluation
The experiments are robust, utilizing two distinct continual learning benchmarks that reflect real-world challenges in ASR. The results demonstrate significant improvements over state-of-the-art baselines, with detailed metrics including Word Error Rate (WER) and Backward Transfer (BWT) providing a comprehensive view of performance. The ablation studies further validate the effectiveness of the IHR approach, confirming the importance of the inverse Hessian adjustment in reducing forgetting and improving adaptability.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including model architecture, training protocols, and hyperparameter settings. The availability of a GitHub repository for code and detailed results enhances reproducibility, allowing other researchers to validate and build upon the findings.
Limitations
While the method shows promise, it relies on approximations of the inverse Hessian, which may introduce inaccuracies. Additionally, the approach is primarily tested on specific ASR tasks, and its generalizability to other domains or more complex scenarios remains to be fully explored.
Broader impact
The implications of this work are significant for the development of adaptive ASR systems, particularly in dynamic environments where models must continuously learn from new data without forgetting previous knowledge. The lightweight and memory-free nature of IHR could facilitate the deployment of ASR systems in resource-constrained settings, enhancing their usability and effectiveness across diverse applications. This paper introduces Inverse Hessian Regularization (IHR), a novel memory-free method for continual learning in automatic speech recognition, which effectively reduces forgetting while maintaining strong adaptability to new tasks. The comprehensive evaluation of IHR demonstrates its potential to significantly advance the field of continual learning in ASR, addressing critical challenges faced by existing methods.
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