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Multitask Feature Learning for Low-Resource Query-by-Example Spoken Term Detection
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DOI：10.1109/JSTSP.2017.2764270.png)
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Abstract
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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.
Keywords:
Query-by-example
spoken term detection
multitask learning
bottle-neck feature
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