An improved MMIE training algorithm for speaker-independent, small vocabulary, continuous speech recognition

Recently, Gopalakrishnan et al. (1989) introduced a reestimation formula for discrete HMMs (hidden Markov models) which applies to rational objective functions like the MMIE (maximum mutual information estimation) criterion. The authors analyze the formula and show how its convergence rate can be substantially improved. They introduce a corrective MMIE training algorithm, which, when applied to the TI/NIST connected digit database, has made it possible to reduce the string error rate by close to 50%. Gopalakrishnan's result is extended to the continuous case by proposing a new formula for estimating the mean and variance parameters of diagonal Gaussian densities.<
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