A hybrid fragment / syllable-based system for improved OOV term detection

Abstract

Spoken term detection (STD) is a task for open vocabulary search in large recordings of speech. Although the term detection performance for in-vocabulary (INV) terms has achieved a great improvement, the detection performance for out of vocabulary (OOV) terms is still disappointing. In this paper, we propose to combine fragment-based with syllable-based search into a hybrid STD system for OOV terms. Syllable is a kind of knowledge-based subword while fragment is data-driven. We initially compare their different modeling ability for OOVs. Considering the potential complementarities between them, we explore two methods of fusion: index fusion (combining the triphone indexes of a fragment-based and a syllable-based system) and result fusion (merging search results of the two systems). After the result fusion, we achieve a 9.4% relative improvement on NIST STD06 English conversational telephone speech (CTS) EvalSet in actual term weighted value (ATWV).