Combination of search techniques for improved spotting of OOV keywords

The most common pipelines in keyword spotting involve some kind of speech recognition, which leads to the generation of sets of plausible hypotheses (e.g., word lattices), which are subsequently explored. The case of out-of-vocabulary (OOV) keywords is of special interest, because it requires representing keywords and/or lattices in an alternative format, so that the two can match. A number of techniques for dealing with OOV keywords have appeared in the literature; here, we focus on (i) fuzzy-phonetic search using phonetic confusion networks [1], and (ii) proxy-keyword search [2]. As we demonstrate in this paper, the combination of these two diverse techniques improves the ATWV of OOV keywords by at least 3% on average over the five development languages used in the second year of the IARPA Babel program.