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In this paper, we propose a neural network based distance metric learning method for a better discrimination in the sequence-matching based keyword search (KWS). In this technique, we conduct a version of Dynamic Time Warping (DTW) based similarity search on the speaker independent posteriorgram space. With this, we
In this work, a template-based search approach is adopted for the Keyword Search (KWS) problem on two of the low-resource languages (Turkish and Swahili). In low-resource languages, the use of Large Vocabulary Continuous Speech Recognition (LVCSR) systems in KWS tasks may perform poorly especially on out-of-vocabulary
variants, and terminological variants. The statistical model is learnt on a corpus prepared through automatic construction of feature vectors from data and metadata features. We evaluate the results at two levels; 1) pre-appraisal stage, 2) post-appraisal stage. We compared the results based on the retrieved result sets with
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