Publication Details
Discriminative Semi-supervised Training for Keyword Search in Low Resource Languages
Ng Tim
Grézl František, Ing., Ph.D. (DCGM)
Karakos Damianos (FIT)
Tsakalidis Stavros (FIT)
Nguyen Long
Schwartz Richard (FIT)
semi-supervised training, low resource languages, keyword spotting
This article is about Discriminative Semi-supervised Training for Keyword Search in Low Resource Languages.
In this paper, we investigate semi-supervised training for low resource languages where the initial systems may have high error rate ( 70.0% word eror rate). To handle the lack of data, we study semi-supervised techniques including data selection, data weighting, discriminative training and multilayer perceptron learning to improve system performance. The entire suite of semi-supervised methods presented in this paper was evaluated under the IARPA Babel program for the keyword spotting tasks. Our semi-supervised system had the best performance in the OpenKWS13 surprise language evaluation for the limited condition. In this paper, we describe our work on the Turkish and Vietnamese systems.
@inproceedings{BUT105975,
author="Roger {Hsiao} and Tim {Ng} and František {Grézl} and Damianos {Karakos} and Stavros {Tsakalidis} and Long {Nguyen} and Richard {Schwartz}",
title="Discriminative Semi-supervised Training for Keyword Search in Low Resource Languages",
booktitle="Proceedings of ASRU 2013",
year="2013",
pages="440--445",
publisher="IEEE Signal Processing Society",
address="Olomouc",
isbn="978-1-4799-2755-5",
url="http://www.fit.vutbr.cz/research/groups/speech/publi/2013/hsiao_asru2013_0000440.pdf"
}