REMAP: recursive estimation and maximization of a posteriori probabilities in connectionist speech recognition

In this paper, we brieey describe REMAP, an approach for the training and estimation of posterior probabilities, and report its application to speech recognition. REMAP is a recursive algorithm that is reminiscent of the Expectation Maximization (EM) 5] algorithm for the estimation of data likelihoods. Although very general, the method is developed in the context of a statistical model for transition-based speech recognition using Artificial Neural Networks (ANN) to generate probabilities for Hidden Markov Models (HMMs). In the new approach , we use local conditional posterior probabilities of transitions to estimate global posterior probabilities of word sequences. As with earlier hybrid HMM/ANN systems we have developed, ANNs are used to estimate posterior probabilities. In the new approach, however, the network is trained with targets that are themselves estimates of local posterior probabilities. Initial experimental results support the theory by showing an increase in the estimates of posterior probabilities of the correct sentences after REMAP iterations, and a decrease in error rate for an independent test set.