Improving keyword search by query expansion in a probabilistic framework

Zhipeng Chen, Zhiyang He, Ping Lv, Ji Wu

Abstract: Keyword search (KWS) in speech data has become an important area of research. Speech recognition error and out-ofvocabulary (OOV) problem are two major challenges in KWS. In this paper, a unified probabilistic framework is proposed for query expansion in KWS to counter both problems. The posterior scores of hits are re-estimated with this framework to re-rank hits and to determine decision thresholds. Experiments on Vietnamese conversational telephone speech show that the actual term-weighted value (ATWV) is significantly improved by expanding queries using this framework. Some deeper diagnostic analysis shows that this framework is insensitive to the parameter and is robust in large-scale expansion, where false alarm problem is very common.