Microsegment-based connected digit recognition

By building acoustic phonetic models which explicitly represent as much knowledge of pronunciation in a small domain (the digits) as possible, we can create a recognition system which not only performs well but allows for meaningful error analysis and improvement. An HMM-based recognizer for the digits and a few associated words was constructed in accord with these principles. About 65 phonetic models were trained on 140 carefully labeled utterances, then iteratively trained on unlabeled data under orthographic supervision. The basic system achieved less than 3% word error rate on digit strings of unknown length from unseen test speakers, and 1.4% on 7-digit strings of known length. This is competitive with word-based models using the same HMM engine and similar parameter settings. As an R&D system, it allows meaningful analysis of errors and relatively straightforward means of improvement