Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR

The paper investigates whether continual learning regularization is necessary for financial speech recognition. Using Whisper‑small adapted to the Earnings22 benchmark, the authors find that all tested methods actually improve performance on new financial data rather than causing catastrophic forgetting, indicating that task complementarity makes regularization counterproductive. When regularization is required, a layer‑wise mini‑KFAC approach yields lower word error rates and more stable optimization than diagonal EWC.

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• multimodal
• benchmarks