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Backchannel Behavior Influences the Perceived Personality of Human and Artificial Communication Partners
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TurnGPT : a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog
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TurnGPT: a Transformer-based Language Model for Predicting Turn-taking in Spoken Dialog ...
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KTH Tangrams: A Dataset for Research on Alignment and Conceptual Pacts in Task-Oriented Dialogue
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Fundamental frequency accommodation in multi-party human-robot game interactions : The effect of winning or losing
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Skantze, Gabriel; Ibrahim, Omnia; Dellwo, Volker. - : KTH, Tal, musik och hörsel, TMH, 2019. : URPP Language and Space, University of Zurich, Switzerland, 2019. : Department of comparative linguistics, University of Zurich, Switzerland, 2019. : Department of computational linguistics, University of Zurich, Switzerland, 2019
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Multimodal Continuous Turn-Taking Prediction Using Multiscale RNNs ...
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A Multimodal Corpus for Mutual Gaze and Joint Attention in Multiparty Situated Interaction
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Kontogiorgos, Dimosthenis; Avramova, Vanya; Alexanderson, Simon. - : KTH, Tal, musik och hörsel, TMH, 2018. : KTH, 2018. : Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, 2018. : Paris, 2018
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Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks
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Skantze, Gabriel. - : KTH, Tal-kommunikation, 2017. : Saarbrucken, Germany, 2017
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Abstract:
Previous models of turn-taking have mostly been trained for specific turn-taking decisions, such as discriminating between turn shifts and turn retention in pauses. In this paper, we present a predictive, continuous model of turn-taking using Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN). The model is trained on human-human dialogue data to predict upcoming speech activity in a future time window. We show how this general model can be applied to two different tasks that it was not specifically trained for. First, to predict whether a turn-shift will occur or not in pauses, where the model achieves a better performance than human observers, and better than results achieved with more traditional models. Second, to make a prediction at speech onset whether the utterance will be a short backchannel or a longer utterance. Finally, we show how the hidden layer in the network can be used as a feature vector for turn-taking decisions in a human-robot interaction scenario. ; QC 20210927
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Keyword:
Computer Sciences; Datavetenskap (datalogi); Language Technology (Computational Linguistics); Språkteknologi (språkvetenskaplig databehandling)
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URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214443
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Tutoring Robots
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In: IFIP Advances in Information and Communication Technology ; 9th International Summer Workshop on Multimodal Interfaces (eNTERFACE) ; https://hal.inria.fr/hal-01350740 ; 9th International Summer Workshop on Multimodal Interfaces (eNTERFACE), Jul 2013, Lisbon, Portugal. pp.80-113, ⟨10.1007/978-3-642-55143-7_4⟩ (2013)
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Error Handling in Spoken Dialogue Systems : Managing Uncertainty, Grounding and Miscommunication
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Skantze, Gabriel. - : KTH, Tal, musik och hörsel, TMH, 2007. : Stockholm : KTH, 2007
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