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In this paper, we present a novel keyword spotting (KWS) method derived from traditional acoustic KWS. The advantage of this method is that it doesn't need any manually transcribed data to train the acoustic model, so it can be deployed fast for KWS task dealing with small languages and dialectal speech, which the
This paper presents the Swahili keyword search system developed by the THUEE team for the OpenKWS15 evaluation, which is conducted by NIST under the IARPA Babel program. There are several highlights in the development of the system, including automatic generation of the pronunciation lexicon, aggressive data
keywords which are used as features to distinguish different sports. Finally, based on the keyword spotting (KWS) results and specific keywords selected for each kind of sports, a score ranking strategy is designed for conducting classification automatically. For robust KWS in our system, adaptation techniques for acoustic
Multilingual (ML) representations play a key role in building speech recognition systems for low resource languages. The IARPA sponsored BABEL program focuses on building speech recognition (ASR) and keyword search (KWS) systems in over 24 languages with limited training data. The most common mechanism to derive ML
The aim of the spoken term detection task is to find the occurrence of user-entered keywords in an archive of audio recordings. The kind of techniques that are used usually are vocabulary-independent, using only the acoustic information available. In this scenario, however, we rely exclusively on the acoustic model
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