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• W2767122664 abstract "We propose a novel technique that learns a low-dimensional feature representation from unlabeled data of a target language, and labeled data from a nontarget language. The technique is studied as a solution to query-by-example spoken term detection (QbE-STD) for a low-resource language. We extract low-dimensional features from a bottle-neck layer of a multitask deep neural network, which is jointly trained with speech data from the low-resource target language and resource-rich nontarget language. The proposed feature learning technique aims to extract acoustic features that offer phonetic discriminability. It explores a new way of leveraging cross-lingual speech data to overcome the resource limitation in the target language. We conduct QbE-STD experiments using the dynamic time warping distance of the multitask bottle-neck features between the query and the search database. The QbE-STD process does not rely on an automatic speech recognition pipeline of the target language. We validate the effectiveness of multitask feature learning through a series of comparative experiments." @default.
• W2767122664 created "2017-11-10" @default.
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• W2767122664 date "2017-12-01" @default.
• W2767122664 modified "2023-09-24" @default.
• W2767122664 title "Multitask Feature Learning for Low-Resource Query-by-Example Spoken Term Detection" @default.
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• W2767122664 doi "https://doi.org/10.1109/jstsp.2017.2764270" @default.
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