Maximum entropy models for speech confidence estimation

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.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Claudio Estienne, Alberto Sanchís, Alfons Juan, Enrique Vidal
• Keywords: Basic Model, Confidence Estimation, ICASSP 2008, Model Takes Information, Signal Processing