Maximum mutual information estimation of HMM parameters for continuous speech recognition using the N-best algorithm

An application of discriminative training methods, maximum mutual information (MMI) training, to large-vocabulary continuous speech recognition is described. An algorithm is developed for efficient MMI estimation of HMM parameters, including exponential codebook coefficients, which cannot be estimated using maximum likelihood (ML) methods. Continuous speech recognition performance of the BYBLOS system on the DARPA 1000-word resource management speech corpus is presented.<
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