1. A method for generating an acoustic confusability measure, said method comprising the steps of: receiving as input a corpus, comprising a set of utterances with corresponding reliable transcriptions; recognizing, via an automatic speech recognition system, at least one utterance among the set of utterances to yield a recognized utterance, wherein said recognized utterance includes at least one decoded frame sequence; coalescing identical sequential phonemes of said at least one decoded frame sequence to yield at least one decoded phoneme sequence; determining, for each corresponding reliable transcription, at least one pronunciation, wherein said at least one pronunciation includes at least one true phoneme sequence; generating as output said recognized corpus comprising for each said recognized utterance at least said at least one decoded phoneme sequence and said at least one true phoneme sequence; and generating an empirically derived acoustic confusability measure from said recognized corpus, said empirically derived acoustic confusability measure comprising a family of probability models Π={p(d|t)} wherein each of d and t are phonemes drawn from an augmented phoneme alphabet Φ′.