语音查询项检索中的两阶段得分规整方法*

得分规整为语音查询项检索系统中的必要过程，文中提出两阶段得分规整方法.先引入rank-p和relative-to-max这2个特征至区分性得分规整方法中，使正确候选结果和错误候选结果的置信度得分区分性更大，更易进行关键词确认.再应用基于优化查询项权重代价指标的得分规整方法得到最优的语音查询项检索性能.实验表明，文中方法同时利用区分性和基于优化查询项权重代价指标得分规整方法的优点，相比最佳单一得分规整方法性能更优.

Two-Stage Score Normalization Method for Spoken Term Detection

Score normalization is an essential part for a spoken term detection (STD) system. In this paper, a two-stage score normalization method is proposed. Firstly, two features, rank-p and relative-to-max, are introduced into a discriminative score normalization method to get more discriminative confidence scores between correct and wrong candidate words, and thus the keyword verification is more accurate. Secondly, a term-weighted value evaluation metric based normalization method is applied to further optimize the performance of STD. Experimental results show that the proposed method takes advantages of both discrimination and metric-based score normalization methods, and it obtains better performance than the best single score normalization method does.