Automatic Detection and Classification of Head Movements in Face-to-Face Conversations

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Automatic Detection and Classification of Head Movements in Face-to-Face Conversations. / Paggio, Patrizia; Aguirrezabal Zabaleta, Manex; Jongejan, Bart; Navarretta, Costanza.

Proceedings of LREC2020 Workshop "People in language, vision and the mind'' (ONION2020). European Language Resources Association, 2020. s. 15-21.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Paggio, P, Aguirrezabal Zabaleta, M, Jongejan, B & Navarretta, C 2020, Automatic Detection and Classification of Head Movements in Face-to-Face Conversations. i Proceedings of LREC2020 Workshop "People in language, vision and the mind'' (ONION2020). European Language Resources Association, s. 15-21. <https://www.aclweb.org/anthology/2020.onion-1.3>

APA

Paggio, P., Aguirrezabal Zabaleta, M., Jongejan, B., & Navarretta, C. (2020). Automatic Detection and Classification of Head Movements in Face-to-Face Conversations. I Proceedings of LREC2020 Workshop "People in language, vision and the mind'' (ONION2020) (s. 15-21). European Language Resources Association. https://www.aclweb.org/anthology/2020.onion-1.3

Vancouver

Paggio P, Aguirrezabal Zabaleta M, Jongejan B, Navarretta C. Automatic Detection and Classification of Head Movements in Face-to-Face Conversations. I Proceedings of LREC2020 Workshop "People in language, vision and the mind'' (ONION2020). European Language Resources Association. 2020. s. 15-21

Author

Paggio, Patrizia ; Aguirrezabal Zabaleta, Manex ; Jongejan, Bart ; Navarretta, Costanza. / Automatic Detection and Classification of Head Movements in Face-to-Face Conversations. Proceedings of LREC2020 Workshop "People in language, vision and the mind'' (ONION2020). European Language Resources Association, 2020. s. 15-21

Bibtex

@inproceedings{9ec85470935e47a397fed2a4be72f032,
title = "Automatic Detection and Classification of Head Movements in Face-to-Face Conversations",
abstract = "This paper presents an approach to automatic head movement detection and classification in data from a corpus of video-recorded face-to-face conversations in Danish involving 12 different speakers. A number of classifiers were trained with different combinations of visual, acoustic and word features and tested in a leave-one-out cross validation scenario. The visual movement features were extracted from the raw video data using OpenPose, and the acoustic ones using Praat. The best results were obtained by a Multilayer Perceptron classifier, which reached an average 0.68 F1 score across the 12 speakers for head movement detection, and 0.40 for head movement classification given four different classes. In both cases, the classifier outperformed a simple most frequent class baseline as well as a more advanced baseline only relying on velocity features.",
author = "Patrizia Paggio and {Aguirrezabal Zabaleta}, Manex and Bart Jongejan and Costanza Navarretta",
year = "2020",
language = "English",
pages = "15--21",
booktitle = "Proceedings of LREC2020 Workshop {"}People in language, vision and the mind'' (ONION2020)",
publisher = "European Language Resources Association",

}

RIS

TY - GEN

T1 - Automatic Detection and Classification of Head Movements in Face-to-Face Conversations

AU - Paggio, Patrizia

AU - Aguirrezabal Zabaleta, Manex

AU - Jongejan, Bart

AU - Navarretta, Costanza

PY - 2020

Y1 - 2020

N2 - This paper presents an approach to automatic head movement detection and classification in data from a corpus of video-recorded face-to-face conversations in Danish involving 12 different speakers. A number of classifiers were trained with different combinations of visual, acoustic and word features and tested in a leave-one-out cross validation scenario. The visual movement features were extracted from the raw video data using OpenPose, and the acoustic ones using Praat. The best results were obtained by a Multilayer Perceptron classifier, which reached an average 0.68 F1 score across the 12 speakers for head movement detection, and 0.40 for head movement classification given four different classes. In both cases, the classifier outperformed a simple most frequent class baseline as well as a more advanced baseline only relying on velocity features.

AB - This paper presents an approach to automatic head movement detection and classification in data from a corpus of video-recorded face-to-face conversations in Danish involving 12 different speakers. A number of classifiers were trained with different combinations of visual, acoustic and word features and tested in a leave-one-out cross validation scenario. The visual movement features were extracted from the raw video data using OpenPose, and the acoustic ones using Praat. The best results were obtained by a Multilayer Perceptron classifier, which reached an average 0.68 F1 score across the 12 speakers for head movement detection, and 0.40 for head movement classification given four different classes. In both cases, the classifier outperformed a simple most frequent class baseline as well as a more advanced baseline only relying on velocity features.

M3 - Article in proceedings

SP - 15

EP - 21

BT - Proceedings of LREC2020 Workshop "People in language, vision and the mind'' (ONION2020)

PB - European Language Resources Association

ER -

ID: 243519048