Modeling the intonation of discourse segments for improved online dialog ACT tagging

Abstract

Prosody is an important cue for identifying dialog acts. In this paper, we show that modeling the sequence of acoustic-prosodic values as n-gram features with a maximum entropy model for dialog act (DA) tagging can perform better than conventional approaches that use coarse representation of the prosodic contour through acoustic correlates of prosody. We also propose a discriminative framework that exploits preceding context in the form of lexical and prosodic cues from previous discourse segments. Such a scheme facilitates online DA tagging and offers robustness in the decoding process, unlike greedy decoding schemes that can potentially propagate errors. Using only lexical and prosodic cues from 3 previous utterances, we achieve a DA tagging accuracy of 72% compared to the best case scenario with accurate knowledge of previous DA tag, which results in 74% accuracy.