A network model of interpersonal alignment in dialog

In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi

Combining heterogeneous inputs for the development of adaptive and multimodal interaction systems

In this paper we present a novel framework for the integration of visual sensor networks and speech-based interfaces. Our proposal follows the standard reference architecture in fusion systems (JDL), and combines different techniques related to Artificial Intelligence, Natural Language Processing and User Modeling to provide an enhanced interaction with their users. Firstly, the framework integrates a Cooperative Surveillance Multi-Agent System (CS-MAS), which includes several types of autonomous agents working in a coalition to track and make inferences on the positions of the targets. Secondly, enhanced conversational agents facilitate human-computer interaction by means of speech interaction. Thirdly, a statistical methodology allows modeling the user conversational behavior, which is learned from an initial corpus and improved with the knowledge acquired from the successive interactions. A technique is proposed to facilitate the multimodal fusion of these information sources and consider the result for the decision of the next system action.This work was supported in part by Projects MEyC TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS S2009/TIC-1485Publicad

Proceedings

Proceedings of the 3rd Nordic Symposium on Multimodal Communication. Editors: Patrizia Paggio, Elisabeth Ahlsén, Jens Allwood, Kristiina Jokinen, Costanza Navarretta. NEALT Proceedings Series, Vol. 15 (2011), vi+87 pp. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/22532

Listeners use intonational phrase boundaries to project turn ends in spoken interaction

In conversation, turn transitions between speakers often occur smoothly, usually within a time window of a few hundred milliseconds. It has been argued, on the basis of a button-press experiment [De Ruiter, J. P., Mitterer, H., & Enfield, N. J. (2006). Projecting the end of a speaker's turn: A cognitive cornerstone of conversation. Language, 82(3):515–535], that participants in conversation rely mainly on lexico-syntactic information when timing and producing their turns, and that they do not need to make use of intonational cues to achieve smooth transitions and avoid overlaps. In contrast to this view, but in line with previous observational studies, our results from a dialogue task and a button-press task involving questions and answers indicate that the identification of the end of intonational phrases is necessary for smooth turn-taking. In both tasks, participants never responded to questions (i.e., gave an answer or pressed a button to indicate a turn end) at turn-internal points of syntactic completion in the absence of an intonational phrase boundary. Moreover, in the button-press task, they often pressed the button at the same point of syntactic completion when the final word of an intonational phrase was cross-spliced at that location. Furthermore, truncated stimuli ending in a syntactic completion point but lacking an intonational phrase boundary led to significantly delayed button presses. In light of these results, we argue that earlier claims that intonation is not necessary for correct turn-end projection are misguided, and that research on turn-taking should continue to consider intonation as a source of turn-end cues along with other linguistic and communicative phenomena

マルチモーダル音声対話システムでの先進的コミュニケーションのためのユーザ状態推定

Tohoku University伊藤彰則課

French Face-to-Face Interaction: Repetition as a Multimodal Resource

International audienceIn this chapter, after presenting the corpus as well as some of theannotations developed in the OTIM project, we then focus on the specificphenomenon of repetition. After briefly discussing this notion, we showthat different degrees of convergence can be achieved by speakersdepending on the multimodal complexity of the repetition and on thetiming in between the repeated element and the model. Although we focusmore specifically on the gestural level, we present a multimodal analysis ofgestural repetitions in which we met several issues linked to multimodalannotations of any type. This gives an overview of crucial issues in crosslevellinguistic annotation, such as the definition of a phenomenonincluding formal and/or functional categorization