Abstract
We propose a probabilistic model for estimating word confidence by fusing predictor features. Starting from the Maximum Entropy (ME) method, we first prove that ME model is equivalent to the best model with certain form to the Minimum Expected Cross Entropy (MECE) criterion. Under the MECE criterion, We extend the form of ME model by introducing a hidden state. We call the new model Hidden-State Maximum Entropy (HSME) model. In a keyword-spotting task, we combine predictor features from both phonetic and word-level systems. Compared to lattice posterior alone, recall at 80% precision is improved from 38.1% to 49.5% on voicemail and from 37.1% to 51.9% on Switchboard. Compared with other fusion methods, HSME consistently outperforms decision tree, and most cases SVM.