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. Analysis shows that both approaches possess respective advantages and shortcomings, and that their combination has the potential to provide an improved confidence estimation. Experiments conducted on meeting data confirm our analysis and show considerable performance improvement with the combined approach, in particular for out-of-vocabulary spoken term detection with stochastic pronunciation modeling.
Handling overlaps in spoken term detection
ICASSP 2011, 36th International Conference on Acoustics, Speech and Signal Processing, May 22-27, 2011, Prague, Czech Republic
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