Efficient empirical determination, computation, and use of acoustic confusability measures

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′.