Abstract：To improve the precision of the spoken term detection system, a term specific thresholding method based on improved score distribution is presented. At the decision stage of the system, different thresholds are set for every query according to the posterior scores. The distribution of all posterior scores retrieved for a query term is modeled by exponential mixture model. The parameters are estimated by the expectation maximization (EM) algorithm in an unsupervised manner. The threshold value is calculated by Bayes minimum risk rule. Since EM algorithm is sensitive to initial values, K-means clustering is used in the initialization instead of randomization. Posterior scores are firstly divided into two classes, the prior distributions are calculated and the intial values of the model parameters are estimated by maximum likelihood method. The experimental results show that the performance of the proposed thresholding method is better than that of others.
陆梨花，张连海. 一种基于改进得分分布的查询项特定阈值方法*[J]. 模式识别与人工智能, 2015, 28(5): 437-442.
LU Li-Hua, ZHANG Lian-Hai. A Term Specific Thresholding Method Based on Improved Score Distribution. , 2015, 28(5): 437-442.
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