This paper describes the application of dynamic programming (DP) techniques to the problems of building and testing non-word based topic spotters. We use a DP algorithm to find sets of similar phoneme sequence fragments, which we call DP-n-grams, and to detect their occurrences in the training and test data. The ability to use partial matches means that that the fragments are longer and more meaningful than the phoneme n-grams that have been tried previously. Detection probabilities of over 90% with less than 10% probability of a false alarm are achieved for seven target categories. Reports about bridges and pontoons are detected with 90% probability at a probability of false alarm of less than 1%.