Improving utterance verification using a smoothed naive Bayes model

Utterance verification can be seen as a conventional pattern classification problem in which a feature vector is obtained for each hypothesized word in order to classify it as either correct or incorrect. It is unclear, however, which predictor (pattern) features and classification model should be used. Regarding the features, we have proposed a new feature, called word trellis stability (WTS), that can be profitably used in conjunction with more or less standard features such as acoustic stability. This is confirmed in this paper, where a smoothed naive Bayes classification model is proposed to adequately combine predictor features. On a series of experiments with this classification model and several features, we have found that the results provided by each feature alone are outperformed by certain combinations. In particular, the combination of the two above-mentioned features has been consistently found to give the most accurate result in two verification tasks.