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.

引用

页数：5

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