Subword-based spoken term detection in audio course lectures

This paper investigates spoken term detection (STD) from audio recordings of course lectures obtained from an existing media repository. STD is performed from word lattices generated offline using an automatic speech recognition (ASR) system configured from a meetings domain. An efficient STD approach is presented where lattice paths which are likely to contain search terms are identified and an efficient phone based distance is used to detect the occurrence of search terms in phonetic expansions of promising lattice paths. STD and ASR results are reported for both in-vocabulary (IV) and out-of-vocabulary (OOV) search terms in this lecture speech domain.