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We propose a new discriminative confidence measurement approach based on an evolution strategy for spoken term detection (STD). Our evolutionary algorithm, named evolutionary discriminant analysis (EDA), optimizes classification errors directly, which is a salient advantage compared with some conventional discriminative models which optimize objective functions based on certain class encoding, e.g...
Spoken term detection (STD) systems usually arrive at many overlapping detections which are often addressed with some pragmatic approaches, e.g. choosing the best detection to represent all the overlaps. In this paper we present a theoretical study based on a concept of acceptance space. In particular, we present two confidence estimation approaches based on Bayesian and evidence perspectives respectively...
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