DISCRIMINATIVE SCORE NORMALIZATION FOR KEYWORD SEARCH DECISION

被引：0
|
作者：
Van Tung Pham [1 ]
Xu, Haihua [1 ]
Chen, Nancy F.
Sivadas, Sunil
Lim, Boon Pang
Chng, Eng Siong [1 ]
Li, Haizhou
机构：
[1] Nanyang Technol Univ, Singapore 639798, Singapore
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2014年
关键词：
score normalization; spoken term detection (STD); keyword spotting; confidence estimation; discriminative modeling; under-resourced languages;
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Many keyword search (KWS) systems make "hit/false alarm (FA)" decisions based on the lattice-based posterior probability, which is incomparable across keywords. Therefore, score normalization is essential for a KWS system. In this paper, we investigate the integration of two novel features, ranking-score and relative-to-max, into a discriminative score normalization method. These features are extracted by considering all competing hypotheses of a putative detection. A metric-based normalization method is also applied as a post-processing step to further optimize the term-weighted value (TWV) evaluation metric. We report empirical improvements over standard baselines using the Vietnamese data from IARPA's Babel program in the NIST OpenKWS13 Evaluation setup.
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页数：5
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