On a model-robust training method for speech recognition

Training methods for designing better decoders are compared. The training problem is considered as a statistical parameter estimation problem. In particular, the conditional maximum likelihood estimate (CMLE), which estimates the parameter values that maximize the conditional probability of words given acoustics during training, is compared to the maximum-likelihood estimate, which is obtained by maximizing the joint probability of the words and acoustics. For minimizing the decoding error rate of the (optimal) maximum a posteriori probability (MAP) decoder, it is shown that the CMLE (or maximum mutual information estimate, MMIE) may be preferable when the model is incorrect. In this sense, the CMLE/MMIE appears more robust than the MLE