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Maximum Entropy Models for Speech Confidence Estimation. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008. pp. 4421-4424.In this work we implement a confidence estimation system based on a Naive Bayes classifier, by using the maximum entropy paradigm. The model takes information from various sources including a set of scores which have proved to be useful in confidence estimation tasks. Two different approaches are modeled. First a basic model which takes advantages of smoothing techniques used in a previous work, and second an optimized model, which is designed to hold a set of very few but essential characteristics of the model, without decrease in the performance. A considerably reduction in the number of parameters is obtained compared to the basic model. Both models are evaluated with two different corpora and compared to a model previously developed.