Detection target dependent score calibration for language recognition

SESSION 4: Speaker and language recognition – scoring, confidences and calibration

Added: 14. 7. 2010 11:08, Author: Raymond W. M. Ng (The Chinese University of Hong Kong), Cheung-Chi Leung (Institute for Infocomm Research), Tan Lee (The Chinese University of Hong Kong), Bin Ma (Institute for Infocomm Research), Haizhou Li (Institute for Infocomm Research, Singapore; University of Eastern Finland), Length: 0:25:08

Based on the conventional score calibration techniques with gaussian backend and logistic regression of the relative likelihood scores, this paper proposes a method of score calibration specific to a subset of related languages. Detection scores to two related languages are considered as two sources with similar and complementary information. In the proposed score calibration, an optimal linear combination of these two sources is derived. Experiments to NIST LRE 2009 with the proposed method give an equal error rate of 3.33%, which is a 25.2% relative reduction compared with the results from globally calibrated scores. Errors in differentiating two related languages can also be reduced by some modifications in parameter optimization.

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