Discriminative score normalization for keyword search decision

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...

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Bibliographic Details
Published in2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 7078 - 7082
Main Authors Van Tung Pham, Haihua Xu, Chen, Nancy F., Sivadas, Sunil, Boon Pang Lim, Eng Siong Chng, Haizhou Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
confidence estimation
discriminative modeling
Feature extraction
Keyword search
keyword spotting
Lattices
Measurement
NIST
score normalization
Speech
Speech processing
spoken term detection (STD)
under-resourced languages
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Summary: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.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6854973