Free energy for speech recognition

Traditionally, speech recognizers have used a strictly Bayesian paradigm for finding the best hypothesis from amongst all possible hypotheses for the data to be recognized. The Bayes classification rule has been shown to be optimal when the class distributions represent the true distributions of the data to be classified. In reality, however, this condition is often not satisfied - the classifier itself is trained on some training data and may be deployed to classify data whose statistical characteristics are different from the training data. The Bayes classification rule may result in suboptimal performance under these conditions of mismatch. Classification may benefit from the use of modified classification rules in this case. The use of entropy as an optimization criterion for various classification tasks has been well established in the literature. In this paper we show that free energy, a thermodynamic concept directly related to entropy, can also be used as an objective criterion in classification. Furthermore, we show how this novel classification scheme can be used in the framework of existing Bayesian classification schemes implemented in current speech recognizers by simply modifying the class distributions a priori. Pilot experiments show that minimization of free energy results in more accurate recognition under conditions of mismatch.