Scaling few-shot spoken word classification with generative meta-continual learning
Few-shot spoken word classification has largely been developed for applications where a small number of classes is considered, and so the potential of larger-scale few-shot spoken word classification remains untapped. This paper investigates the potential of a spoken word classifier to sequentially learn to distinguish between 1000 classes when it is given only five shots per class. We demonstrate that this scaling capability exists by training a model using the Generative Meta-Continual Learning (GeMCL) algorithm and comparing it to repeatedly trained or finetuned baselines. We find that GeMCL produces exceptionally stable performance, and although it does not always outperform a repeate...
| Authors | Louise Beyers, Batsirayi Mupamhi Ziki, Ruan van der Merwe |
|---|---|
| Primary category | cs.CL |
| Published | 13 May 2026 |
| Last updated at source | 4 June 2026 |
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