System and keyword dependent fusion for spoken term detection

System combination (or data fusion1) is known to provide significant improvement for spoken term detection (STD). The key issue of the system combination is how to effectively fuse the various scores of participant systems. Currently, most system combination methods are system and keyword independent, i.e. they use the same arithmetic functions to combine scores for all keywords. Although such strategy improve keyword search performance, the improvement is limited. In this paper we first propose an arithmetic-based system combination method to incorporate the system and keyword characteristics into the fusion procedure to enhance the effectiveness of system combination. The method incorporates a system-keyword dependent property, which is the number of acceptances in this paper, into the combination procedure. We then introduce a discriminative model to combine various useful system and keyword characteristics into a general framework. Improvements over standard baselines are observed on the Vietnamese data from IARPA Babel program with the NIST OpenKWS13 Evaluation setup.