Nowadays, one of the challenges of keyword spotting is the issue of out-of-vocabulary (OOV) word. The detection performance for OOV word is considerably worse than for in-vocabulary (INV) word due to its high degree of uncertainty in pronunciation. This paper presents a method to improve the OOV word detection performance by incorporating query expansion into dynamic match. We initially compare the joint-multigram model based query expansion and the minimum edit distance (MED) based dynamic match for OOV word. Considering the potential mutual complementarity between them, we propose two methods of fusion. One is result fusion: performing a parallel OOV word detection with query expansion and dynamic match individually and then merging search results of the two systems. Another is confidence fusion: combining MED and the pronunciation score together as a hybrid confidence measure to implement OOV word detection and verification. Tests show that the second fusion method is more efficient and the figure of merit achieves 19.8% improvement relatively.