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Data-driven communicative behaviour generation : a survey

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Abstract
The development of data-driven behaviour generating systems has recently become the focus of considerable attention in the fields of human-agent interaction and human-robot interaction. Although rule-based approaches were dominant for years, these proved inflexible and expensive to develop. The difficulty of developing production rules, as well as the need for manual configuration to generate artificial behaviours, places a limit on how complex and diverse rule-based behaviours can be. In contrast, actual human-human interaction data collected using tracking and recording devices makes humanlike multimodal co-speech behaviour generation possible using machine learning and specifically, in recent years, deep learning. This survey provides an overview of the state of the art of deep learning-based co-speech behaviour generation models and offers an outlook for future research in this area.
Keywords
SPEECH, ERROR, MODELS, MOTION, CLASSIFICATION, ALGORITHMS, UTTERANCES, GESTURES, NETWORK, JOINT, Datasets, neural networks, data-driven behaviour generation

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MLA
Oralbayeva, Nurziya, et al. “Data-Driven Communicative Behaviour Generation : A Survey.” ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION, vol. 13, no. 1, 2024, doi:10.1145/3609235.
APA
Oralbayeva, N., Aly, A., Sandygulova, A., & Belpaeme, T. (2024). Data-driven communicative behaviour generation : a survey. ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION, 13(1). https://doi.org/10.1145/3609235
Chicago author-date
Oralbayeva, Nurziya, Amir Aly, Anara Sandygulova, and Tony Belpaeme. 2024. “Data-Driven Communicative Behaviour Generation : A Survey.” ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION 13 (1). https://doi.org/10.1145/3609235.
Chicago author-date (all authors)
Oralbayeva, Nurziya, Amir Aly, Anara Sandygulova, and Tony Belpaeme. 2024. “Data-Driven Communicative Behaviour Generation : A Survey.” ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION 13 (1). doi:10.1145/3609235.
Vancouver
1.
Oralbayeva N, Aly A, Sandygulova A, Belpaeme T. Data-driven communicative behaviour generation : a survey. ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION. 2024;13(1).
IEEE
[1]
N. Oralbayeva, A. Aly, A. Sandygulova, and T. Belpaeme, “Data-driven communicative behaviour generation : a survey,” ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION, vol. 13, no. 1, 2024.
@article{01HY305GPZ6AH33Z8E1JEZ5E8G,
  abstract     = {{The development of data-driven behaviour generating systems has recently become the focus of considerable attention in the fields of human-agent interaction and human-robot interaction. Although rule-based approaches were dominant for years, these proved inflexible and expensive to develop. The difficulty of developing production rules, as well as the need for manual configuration to generate artificial behaviours, places a limit on how complex and diverse rule-based behaviours can be. In contrast, actual human-human interaction data collected using tracking and recording devices makes humanlike multimodal co-speech behaviour generation possible using machine learning and specifically, in recent years, deep learning. This survey provides an overview of the state of the art of deep learning-based co-speech behaviour generation models and offers an outlook for future research in this area.}},
  articleno    = {{2}},
  author       = {{Oralbayeva, Nurziya and  Aly, Amir and  Sandygulova, Anara and Belpaeme, Tony}},
  issn         = {{2573-9522}},
  journal      = {{ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION}},
  keywords     = {{SPEECH,ERROR,MODELS,MOTION,CLASSIFICATION,ALGORITHMS,UTTERANCES,GESTURES,NETWORK,JOINT,Datasets,neural networks,data-driven behaviour generation}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{39}},
  title        = {{Data-driven communicative behaviour generation : a survey}},
  url          = {{http://doi.org/10.1145/3609235}},
  volume       = {{13}},
  year         = {{2024}},
}

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