TY - GEN

T1 - Creating a Corpus of Gestures and Predicting the Audience Response based on Gestures in Speeches of Donald Trump

AU - Ruf, Verena

AU - Navarretta, Costanza

PY - 2020

Y1 - 2020

N2 - 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 inorder to predict audience response, such as applause. The aim of this study is to explore the role of speech pauses and gestures aloneas 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 secondsare 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.

AB - 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 inorder to predict audience response, such as applause. The aim of this study is to explore the role of speech pauses and gestures aloneas 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 secondsare 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.

M3 - Article in proceedings

SP - 1074

EP - 1081

BT - Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)

PB - European Language Resources Association

CY - Marsaille, France

ER -