Novel acoustic features for automatic dialog-act tagging

This paper presents 57 new acoustic features for automatic dialog-act tagging. The features are intended to be richer than and complementary to the traditional cumulative statistics of intonation. Some of our novel contributions include feature normalization with respect to neighboring utterances, incorporation of periodicity and formant features, modeling of cognitive phenomena such as hesitations, and utterance-level aggregation of short-term acoustic effects. The proposed features are applied to 3-way dialog-act tagging and question detection using two databases (British-English call-center conversations and Switchboard), and compared with a popular cumulative-statistics baseline using logistic-regression models. Our features are found to be significantly better than and complementary to the baseline, on average, achieving an absolute performance gain of ~5-6%. Combined feature ranking reveals that about 75% of the top 20 features belong to the proposed feature set, and that the two corpora differ in their feature preferences despite similar overall performance.