@inproceedings{ruf-navarretta-2020-creating,
title = "Creating a Corpus of Gestures and Predicting the Audience Response based on Gestures in Speeches of {D}onald Trump",
author = "Ruf, Verena and
Navarretta, Costanza",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.136",
pages = "1081--1088",
abstract = "Gestures are an important component of non{--}verbal communication. This has an increasing potential in human{--}computer interaction. For example, Navarretta (2017b) uses sequences of speech and pauses together with co{--}speech gestures produced by Barack Obama in order to predict audience response, such as applause. The aim of this study is to explore the role of speech pauses and gestures alone as predictors of audience reaction without other types of speech information. For this work, we created a corpus of speeches held by Donald Trump before and during his time as president between 2016 and 2019. The data were transcribed with pause information and co{--}speech gestures were annotated as well as audience responses. Gestures and long silent pauses of the duration of at least 0.5 seconds are the input of computational models to predict audience reaction. The results of this study indicate that especially head movements and facial expressions play an important role and they confirm that gestures can to some extent be used to predict audience reaction independently of speech.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Gestures are an important component of non–verbal communication. This has an increasing potential in human–computer interaction. For example, Navarretta (2017b) uses sequences of speech and pauses together with co–speech gestures produced by Barack Obama in order to predict audience response, such as applause. The aim of this study is to explore the role of speech pauses and gestures alone as predictors of audience reaction without other types of speech information. For this work, we created a corpus of speeches held by Donald Trump before and during his time as president between 2016 and 2019. The data were transcribed with pause information and co–speech gestures were annotated as well as audience responses. Gestures and long silent pauses of the duration of at least 0.5 seconds are the input of computational models to predict audience reaction. The results of this study indicate that especially head movements and facial expressions play an important role and they confirm that gestures can to some extent be used to predict audience reaction independently of speech.</abstract>
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%0 Conference Proceedings
%T Creating a Corpus of Gestures and Predicting the Audience Response based on Gestures in Speeches of Donald Trump
%A Ruf, Verena
%A Navarretta, Costanza
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F ruf-navarretta-2020-creating
%X Gestures are an important component of non–verbal communication. This has an increasing potential in human–computer interaction. For example, Navarretta (2017b) uses sequences of speech and pauses together with co–speech gestures produced by Barack Obama in order to predict audience response, such as applause. The aim of this study is to explore the role of speech pauses and gestures alone as predictors of audience reaction without other types of speech information. For this work, we created a corpus of speeches held by Donald Trump before and during his time as president between 2016 and 2019. The data were transcribed with pause information and co–speech gestures were annotated as well as audience responses. Gestures and long silent pauses of the duration of at least 0.5 seconds are the input of computational models to predict audience reaction. The results of this study indicate that especially head movements and facial expressions play an important role and they confirm that gestures can to some extent be used to predict audience reaction independently of speech.
%U https://aclanthology.org/2020.lrec-1.136
%P 1081-1088
Markdown (Informal)
[Creating a Corpus of Gestures and Predicting the Audience Response based on Gestures in Speeches of Donald Trump](https://aclanthology.org/2020.lrec-1.136) (Ruf & Navarretta, LREC 2020)
ACL