State of the art vocabulary-independent spoken term detection methods are typically based on variants of the dynamic time warping (DTW) algorithm since DTW, being based on acoustic sequence matching, allows robust retrieval in settings with scarcity of linguistic resources. However, the DTW comes with a high computational cost which limits its practicality in a deployed server. To this end, we investigate the efficacy of subsampling and propose a neural network architecture to reduce the computational load of DTW-based keyword search. We use a time-subsampled RNN to reduce the frame rate of the document as well as the dimensionality of representation while training it to maintain the cost incurred along the DTW alignment path, thus allowing us to reduce the computational complexity (both space and time) of the search algorithm.

Experiments on the Turkish and Zulu limited language packs of the IARPA Babel program show that the proposed methods allow considerable reduction in CPU time (88 times) and memory usage (18 times) without significant loss in search accuracy (0.0270 ATWV). Moreover, even at very high compression levels with lower search precision, high recall rates are maintained, allowing the potential of multi-resolution search.