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...
Saved in:
Published in | 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 7078 - 7082 |
---|---|
Main Authors | , , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.05.2014
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
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 |