Multilingual generative meta-learning for spoken word classification shows small gains over monolingual models, with unique data volume mattering more than the number of languages.
Learning to C ontinually L earn with the B ayesian P rinciple
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GeMCL achieves stable 1000-class few-shot spoken word classification with 5 shots per class, comparable to finetuned HuBERT but 2000x faster adaptation using less data and time.
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Does language matter for spoken word classification? A multilingual generative meta-learning approach
Multilingual generative meta-learning for spoken word classification shows small gains over monolingual models, with unique data volume mattering more than the number of languages.
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Scaling few-shot spoken word classification with generative meta-continual learning
GeMCL achieves stable 1000-class few-shot spoken word classification with 5 shots per class, comparable to finetuned HuBERT but 2000x faster adaptation using less data and time.