Introduces a representation-geometry-based taxonomy for continual learning in speech and audio, identifies mismatches with current CL assumptions in foundation models, and lists open challenges.
Adapting where it matters: Depth-aware adaptation for efficient multilingual speech recognition in low-resource languages
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Rethinking Continual Learning for Speech and Audio: A Representation-Centric Taxonomy and Open Problems
Introduces a representation-geometry-based taxonomy for continual learning in speech and audio, identifies mismatches with current CL assumptions in foundation models, and lists open challenges.