The design and evaluation of subword-based spoken term detection (STD) systems depends on various factors, such as language, type of the speech to be searched and application scenario. The choice of the subword unit and search approach, however, is oftentimes made regardless of these factors. Therefore, we evaluate two subword STD systems across two data sets with varying properties to investigate the influence of different subword units on STD performance when working with different data types. Results show that on German broadcast news data, constrained search in syllable lattices is effective, whereas fuzzy phone lattice search is superior in more challenging English conversational telephone speech. By combining the key features of the two systems at an early stage, we achieve improvements in Figure of Merit of up to 13.4% absolute on the German data. We also show that the choice of the appropriate evaluation metric is crucial when comparing retrieval performances across systems.