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The transfer of acoustic data across languages has been shown to improve keyword search (KWS) performance in data-scarce settings. In this paper, we propose a way of performing this transfer that reduces the impact of the prevalence of out-of-vocabulary (OOV) terms on KWS in such a setting. We investigate a novel usage of multilingual features for KWS with very little training data in the target languages. The crux of our approach is the use of synthetic phone exemplars to convert the search into a query-by-example task, which we solve with the dynamic time warping algorithm. Using bottleneck features obtained from a network trained multilingually on a set of (source) languages, we train an extended distance metric learner (EDML) for four target languages from the IARPA Babel program (which are distinct from the source languages). Compared with a baseline system that is based on automatic speech recognition (ASR) with a multilingual acoustic model, we observe an average term weighted value improvement of
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