As the posterior probability confidence measure cannot take full advantage of the variations in voice pronunciation, we propose an improved confidence measure based on time and boundary feature. A relaxation rate is introduced to have a flexible selection of the segmental arcs with the same words for the
calculation of confidence by which detections are rejected. On this basis, a keyword spotting system with large keyword table based on lattice is designed. An improved dynamic time warping (DTW) algorithm is used for keyword matching through the lattice to generate maximum keyword hypotheses. We have applied the proposed confidence measure in keyword verification. The results show that the equal error rate (EER) achieves 7% relative improvement compared to the mainstream method of calculation.
LI Wen-xin, QU Dan, LI Bi-cheng, WANG Bing-xi
. Confidence Measure Based on Time and Boundary Features for Speech Keyword Spotting System[J]. Journal of Applied Sciences, 2012
, 30(6)
: 588
-594
.
DOI: 10.3969/j.issn.0255-8297.2012.06.005
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