Facilitating open vocabulary spoken term detection using a multiple pass hybrid search algorithm

This paper presents an efficient approach to spoken term detection (STD) from unstructured audio recordings using word lattices generated off-line from an automatic speech recognition (ASR) system. The approach facilitates open vocabulary STD and focuses specifically on reducing the difference between detection performance obtained for within-vocabulary (IV) and out-of-vocabulary (OOV) search terms. Improved OOV detection performance is obtained by using a two-pass search procedure. Candidate audio segments are retrieved from an index of word lattice paths in the first pass. Locations of OOV search terms are detected in the second pass from a constrained alignment of phonemic expansions of the query terms with phoneme sequences obtained from acoustic segments using an unconstrained neural network based phone decoder. It is found that the combination of first pass segment retrieval and second pass term verification significantly increases STD performance for OOV query terms with no increase in search time for utterances taken from a lecture speech domain.