Summary of the paper

Title A «Portrait» Approach to Multichannel Discourse
Authors Andrej Kibrik and Olga Fedorova
Abstract This paper contributes to the research field of multichannel discourse analysis. Multimodal discourse analysis explores numerous channels involved in natural communication, such as verbal structure, prosody, gesticulation, facial expression, eye gaze, etc., and treats them as parts of an integral process. Among the key issues in multichannel studies is the question of the individual variation in multichannel behavior. We address this issue with the help of a multichannel resource “Russian Pear Chats and Stories” that is currently under construction ( This corpus is based on a novel methodology of data collection and is produced with the help of state of the art technology including eyetracking. To address the issue of individual variation, we introduce the notion of a speaker’s individual portrait. In particular, we consider the Prosodic Portrait, the Oculomotor Portrait, and the Gesticulation Portrait. The proposed methodology is crucially important for fine-grained annotation procedures as well as for accurate statistic analyses of multichannel data.
Topics Discourse Annotation, Representation And Processing, Prosody, Corpus (Creation, Annotation, Etc.)
Full paper A «Portrait» Approach to Multichannel Discourse
Bibtex @InProceedings{KIBRIK18.127,
  author = {Andrej Kibrik and Olga Fedorova},
  title = "{A «Portrait» Approach to Multichannel Discourse}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
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