Spoken term detection (STD) is an extension of text-based searching that allows users to type keywords and search audio files containing recordings of spoken language. Performance is dependent on many external factors such as the acoustic channel, the language and the confusability of the search term. Unlike text-based searches, the quality of the search term plays a significant role in the overall perception of the usability of the system. In this paper, we present a system that predicts the strength of a search term from its spelling that is based on an analysis of spoken term detection output from several spoken term detection systems that participated in the NIST 2006 STD evaluation. We show that approximately 57% of the correlation can be explained from the search term, but that a significant amount of the confusability is due to other acoustic modeling issues.