Phonetic recognition using hidden Markov models and maximum mutual information training

The application of maximum-mutual-information (MMI) training to hidden Markov models (HMMs) is studied for phonetic recognition. MMI training has been proposed as an alternative to standard maximum-likelihood (ML) training. In practice, MMI training performs better (produces models that are more accurate) than ML training. The fundamental notions of HMM, ML and MMI training are reviewed, and it is shown how MMI training can be applied easily to the case of phonetic models and phonetic recognition. Some computational heuristics are proposed to implement these computations practically. Some experiments (training and recognition) are detailed that show that the phonetic error rate decreases significantly when MMI training is used, as compared with ML training