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arXiv · 2506.16574

Weight Factorization and Centralization for Continual Learning in Speech Recognition

Enes Yavuz Ugan·Ngoc-Quan Pham·Alexander Waibel
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Abstract

Modern neural network based speech recognition models are required to continually absorb new data without re-training the whole system, especially in downstream applications using foundation models, having no access to the original training data. Continually training the models in a rehearsal-free, multilingual, and language agnostic condition, likely leads to catastrophic forgetting, when a seemingly insignificant disruption to the weights can destructively harm the quality of the models. Inspired by the ability of human brains to learn and consolidate knowledge through the waking-sleeping cycle, we propose a continual learning approach with two distinct phases: factorization and centralization, learning and merging knowledge accordingly. Our experiments on a sequence of varied code-switching datasets showed that the centralization stage can effectively prevent catastrophic forgetting by accumulating the knowledge in multiple scattering low-rank adapters.

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Enes Yavuz Ugan, Ngoc-Quan Pham, Alexander Waibel. 2025-06-19. Weight Factorization and Centralization for Continual Learning in Speech Recognition. https://arxiv.org/abs/2506.16574

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