Automatic measures of similarity between sentences or utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the training of such measures, we developed the first collection of human judgments of pragmatic similarity between utterance pairs. 9 judges listened to 220 utterance pairs, each consisting of an utterance extracted from a recorded dialog and a re-enactment of that utterance under various conditions designed to create various degrees of similarity. Each pair was rated on a continuous scale. The average inter-judge correlation was 0.45. We make this data available at https://github.com/divettemarco/PragSim .

The construction of spoken dialog systems today relies heavily on appropriate corpora, but corpus selection is more an art than a science. As interaction style properties govern many aspects of dialog, they have the potential to be useful for relating and comparing corpora. This paper overviews a recently-developed model of interaction styles and shows how it can be used to identify relevant corpus differences, estimate corpus similarity, and flag likely outlier dialogs.

Prosody is essential in human interaction, enabling people to show interest, establish rapport, efficiently convey nuances of attitude or intent, and so on. Some applications that exploit prosodic knowledge have recently shown superhuman performance, and in many respects our ability to effectively model prosody is rapidly advancing. This tutorial will overview the computational modeling of prosody, including recent advances and diverse actual and potential applications.

We collected a corpus of human-human task-oriented dialogs rich in dissatisfaction and built a model that used prosodic features to predict when the user was likely dissatisfied. For utterances this attained a F.25 score of 0.62,against a baseline of 0.39. Based on qualitative observations and failure analysis, we discuss likely ways to improve this result to make it have practical utility.

here is increasing interest in modeling style choices in dialog, for example for enabling dialog systems to adapt to their users. It is commonly assumed that each user has his or her own stable characteristics, but for interaction style the truth of this assumption has not been well examined. I investigated using a vector-space model of interaction styles, derived from the Switchboard corpus of telephone conversations and a broad set of prosodic-behavior features. While most individuals exhibited interaction style tendencies, these were generally far from stable, with a predictive model based on individual tendencies outperforming a speaker-independent model by only 3.6%. The tendencies were somewhat stronger for some speakers, including generally males, and for some dimensions of variation.