Projecting the End of a Speaker's Turn: A Cognitive Cornerstone of Conversation

By J. D. Ruiter, Holger Mitterer, N. Enfield. 2006

399 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/c8b79f5375794a5482454c079f6cfc89f2267933

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Citations in the corpus

The neuropragmatics of 'simple' utterance comprehension: An ERP review (2009, methodology): “Part of this puzzle is that, whereas linguists have lots of time to ponder possible interpretations, much of the everyday pragmatic computation has to be over and done with after a second or so. In conversation, even just waiting until the end of the utterance is no option -- the dynamics of turntak”
Primary and secondary pragmatic functions of pointing gestures (2007, background): “A good example is intonation contour, which can be varied independent of lexico-syntax (cf. De Ruiter et al., 2006).”
Investigating Speech Features for Continuous Turn-Taking Prediction Using LSTMs (2018, neutral): “Given that there is disagreement in the literature as to whether linguistic [21] or acoustic [22] information is more important for predicting turn-taking, our ﬁndings present evidence that acoustic information is generally more useful as a ﬁrst choice for continuous turn-taking prediction.”
Initiating Interactive Turn Spaces in Japanese Conversation: Local Projection and Collaborative Action (2009, background): “The emerging lexico-syntactic structure of a turn provides a crucial resource for projecting points in the turn that are relevant to turntaking (Auer, 2005; de Ruiter et al., 2006).”
What do we mean by prediction in language comprehension? (2015, background): “Critically, evidence that we predict during naturalistic language processing tasks (Brown-Schmidt & Tanenhaus, 2008) and in everyday conversation (de Ruiter et al., 2006), suggests that the utility of predictive pre-activation is relatively high during everyday language processing.”
Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks (2017, background): “Several studies have shown that humans are able to predict upcoming turn-taking events (Tice and Henetz, 2011), and that this prediction facilitates rapid and accurate turn-taking (Ruiter et al., 2006).”
Timing in turn-taking and its implications for processing models of language (2015, methodology): “De Ruiter et al. (2006) extracted turns from a corpus of conversations in Dutch, and got participants to press a button in anticipation of turn endings.”
Pauses, gaps and overlaps in conversations (2010, methodology): “Furthermore, it makes a considerable difference whether the estimates of central tendency are calculated from all betweenspeaker intervals (as in de Ruiter et al., 2006; Norwine & Murphy, 1938; Sellen, 1995) or from gaps and overlaps separately (as in e.g. Brady, 1968; Jaffe & Feldstein, 1970;…”