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Settling operators can significantly boost accuracy on unseen tasks, transforming additional iterations into a performance advantage rather than a risk of degradation.
A novel training-free approach boosts anomalous sound detection scores by over 3.5 points while revealing that conventional development-set metrics can mislead model selection.
Language is a major, underappreciated domain shift factor in spoof detection, with performance varying wildly across languages even under controlled conditions.
