1. In a computer implemented method for determining an empirically derived acoustic confusability measure, an iterative method for development of a probability model family Π={p(d|t)}, comprising: providing a recognized corpus; establishing a termination condition which depends on any of: a number of iterations executed; and closeness of match between a previous and current probability family models; defining a family of decoding costs; setting an iteration count to 0; setting a phoneme pair count to 0; for each entry in the recognized corpus, performing the following steps: constructing a lattice; populating lattice arcs with values drawn from a current family of decoding costs; applying a Bellman-Ford dynamic programming algorithm, or a Dijkstra's shortest path algorithm, to find a shortest path through said lattice, from a source node to a terminal node; and traversing said determined shortest path, wherein for each arc that is traversed, the phoneme pair count is incremented by 1; for each transcription, computing a confidence score which is the sum of a phoneme pair value over all transcriptions paired with an utterance; estimating said probability model family; if the iteration count exceeds 0, testing said termination condition; if said termination condition is satisfied, returning a desired probability model family and stopping; if said termination condition is not satisfied, defining a new family of decoding costs and therefrom a new probability model family; and incrementing said iteration count and repeating.