Detail of publication

Abstract

This paper reports our initial experiments with automatic punctuation annotation from speech. We have focused on Czech broadcast news speech. We employed two statistical models - prosodic model and language model. The prosodic model expresses relationships between prosodic quantities (such as pitch, speaking rate or loudness) and punctuation marks. We tested two implementations of this model -- decision tree and multi-layer perceptron. Hidden-event N-gram models were employed for language modeling. Instead of using an ordinary word-based model, we replaced infrequent word forms by their morphological tags and trained a mixed model. Scores from both models can be combined. The model combining language model with the decision tree yielded superior results. Testing on true words we achieved classification accuracy 95.2% and F-measure 78.2%.