Towards spoken term discovery at scale with zero resources

Citations Over TimeTop 1% of 2010 papers

Abstract

The spoken term discovery task takes speech as input and identifies terms of possible interest. The challenge is to perform this task efficiently on large amounts of speech with zero resources (no training data and no dictionaries), where we must fall back to more basic properties of language. We find that long (∼ 1 s) repetitions tend to be contentful phrases (e.g. University of Pennsylvania) and propose an algorithm to search for these long repetitions without first recognizing the speech. To address efficiency concerns, we take advantage of (i) sparse feature representations and (ii) inherent low occurrence frequency of long content terms to achieve orders-of-magnitude speedup relative to the prior art. We frame our evaluation in the context of spoken document information retrieval, and demonstrate our method’s competence at identifying repeated terms in conversational telephone speech. Index Terms: spoken term discovery, zero resource speech recognition, dotplots

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