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Evolutionary Discriminative Confidence Estimation for Spoken Term Detection

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Executive Summary

Spoken Term Detection (STD) is the task of searching for occurrences of spoken terms in audio archives. It relies on robust confidence estimation to make a hit/False Alarm (FA) decision. In order to optimize the decision in terms of the STD evaluation metric, the confidence has to be discriminative. Multi-Layer Perceptrons (MLPs) and Support Vector Machines (SVMs) exhibit good performance in producing discriminative confidence; however, they are severely limited by the continuous objective functions, and are, therefore, less capable of dealing with complex decision tasks. This leads to a substantial performance reduction when measuring detection of Out-Of-Vocabulary (OOV) terms, where the high diversity in term properties usually leads to a complicated decision boundary.

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