An efficient approach for two-stage open vocabulary spoken term detection

Abstract

This paper investigates indexing strategies for open vocabulary spoken term detection (STD) in a lecture speech domain. STD is performed from word lattices generated offline using an automatic speech recognition (ASR) system configured from a meetings task domain. Indexing of lattice paths is performed to avoid exhaustive search of audio segments which can be impractical for extremely large media repositories. The method is based on constructing a word-based index from these lattices and using an approximate subword-based algorithm for accessing index entries from subword expansions of query terms. Results are presented for an experimental study demonstrating both STD performance and the potential for scaling the indexing strategy to very large collections of audio segments.