Predicting dialogue acts for a speech-to-speech translation system

Abstract

We present the application of statistical language modeling methods for the prediction of the next dialogue act. This prediction is used by different modules of the speech-to-speech translation system VERBMOBIL. The statistical approach uses deleted interpolation of n-gram frequencies as basis and determines the interpolation weights by a modified version of the standard optimization algorithm. Additionally, we present and evaluate different approaches to improve the prediction process, e.g. including knowledge from a dialogue grammar. Evaluation shows that including the speaker information and mirroring the data delivers the best results.