arXiv:2605.13075v3 Announce Type: replace Abstract: 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 repeated
The paper leverages recent advancements in generative models and meta-learning to address the scalability challenges in few-shot learning, a critical step for practical AI applications.
This development suggests a significant leap towards more adaptable and efficient AI systems, capable of learning new concepts with very limited data, which has broad implications for AI deployment in complex environments.
AI models could become much more versatile, capable of rapidly adapting to new tasks and classes without extensive retraining or massive datasets for each new application.
- · AI research labs
- · Generative AI developers
- · Speech recognition companies
- · Vertical AI solution providers
- · Companies reliant on large, static datasets
- · Traditional machine learning approaches
Few-shot learning capabilities in spoken word classification are significantly enhanced, allowing for expansion to 1000 classes with minimal samples.
This improved efficiency and scalability could accelerate the development of personalized voice AI assistants or specialized voice interfaces in diverse domains.
The underlying Generative Meta-Continual Learning approach could generalize to other modalities, leading to accelerated development of adaptable AI across various sensory inputs.
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Read at arXiv cs.CL