A Novel Discriminative Score Calibration Method for Keyword Search

Zhiqiang Lv, Meng Cai, Wei-Qiang Zhang, Jia Liu


The performance of keyword search systems depends heavily on the quality of confidence scores. In this work, a novel discriminative score calibration method has been proposed. By training an MLP classifier employing the word posterior probability and several novel normalized scores, we can obtain a relative improvement of 4.67% for the actual term-weighted value (ATWV) metric on the OpenKWS15 development test dataset. In addition, a LSTM-CTC based keyword verification method has been proposed to supply extra acoustic information. After the information is added, a further improvement of 7.05% over the baseline can be observed.


DOI: 10.21437/Interspeech.2016-606

Cite as

Lv, Z., Cai, M., Zhang, W., Liu, J. (2016) A Novel Discriminative Score Calibration Method for Keyword Search. Proc. Interspeech 2016, 745-749.

Bibtex
@inproceedings{Lv+2016,
author={Zhiqiang Lv and Meng Cai and Wei-Qiang Zhang and Jia Liu},
title={A Novel Discriminative Score Calibration Method for Keyword Search},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-606},
url={http://dx.doi.org/10.21437/Interspeech.2016-606},
pages={745--749}
}