A Bayesian predictive classification approach to robust speech recognition

We introduce a new decision strategy called Bayesian predictive classification (BPC) for robust speech recognition where an unknown mismatch between the training and testing conditions exists. We then propose and focus on one of the approximate BPC approaches called quasi-Bayes predictive classification (QBPC). In a series of comparative experiments where the mismatch is caused by additive white Gaussian noise, we show that the proposed QBPC approach achieves a considerable improvement over the conventional plug-in MAP decision rule.