To Memorize or to Predict: Prominence labeling in Conversational Speech

Abstract

The immense prosodic variation of natural con-versational speech makes it challenging to pre-dict which words are prosodically prominent in this genre. In this paper, we examine a new fea-ture, accent ratio, which captures how likely it is that a word will be realized as prominent or not. We compare this feature with traditional accent-prediction features (based on part of speech and N-grams) as well as with several linguistically mo-tivated and manually labeled information structure features, such as whether a word is given, new, or contrastive. Our results show that the linguistic fea-tures do not lead to significant improvements, while accent ratio alone can yield prediction performance almost as good as the combination of any other sub-set of features. Moreover, this feature is useful even across genres; an accent-ratio classifier trained only on conversational speech predicts prominence with high accuracy in broadcast news. Our results sug-gest that carefully chosen lexicalized features can outperform less fine-grained features. 1