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o r g e r a e r c h e informatique de M o n t r t a l (CRIM) MontrCal, C o m m u n i c a t i o n s Systems GroupABSTRACT In several speech recognition tasks, Maximum Mutual Information estimation (MMIE) of Hidden Markov Model (HMM) parameters can substantially improve recognition results [1,2]. However, it I S usually implemented using ally Practical rec gradient descent, which can have very slow convergence. Recently, Gopalakrishnan et nl [3] intr a reestimation More recently, a different formula for discrete HMMs which s to rational mutual information estimation (M objcctive functions (like t!ie M M I E c 1. We analyze 151. T h e objective function used in the formula and show how its convergence rate can be substantially improved. We introduce our "corrective MMIE training" algorithm, which, when applied to the TI/NIST connected digit database, has allowed us to reduce our string error rate by close to 50%. We extend Gopalakrishnan's result to the continuous case by proposing a new formula for estimating the mean and variance parameters of diagonal Gaussian densities. not intuitively obvious how (1) relates t reducing the error rate. It can be shown assumPtions are met, decoder. However, thes MMIE increases the posteriori proba is also the criterion used in '

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