语音查询项检索中的两阶段得分规整方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201603003

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Abstract 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.

Key words： Spoken Term Detection Score Normalization Discriminative Model Confidence Score

Received: 18 November 2014

ZTFLH:

TN 912.34

Fund:Supported by National Natural Science Foundation of China (No.61403415,61175017)

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	LI Peng
	QU Dan

Cite this article:

LI Peng,QU Dan. Two-Stage Score Normalization Method for Spoken Term Detection[J]. , 2016, 29(3): 216-222.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201603003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I3/216

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