The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked
  Emotions, Cross-Cultural Humour, and Personalisation
Lukas Christ Shahin Amiriparian Alice Baird Alexander Kathan Niklas Müller Steffen Klug Chris Gagne Panagiotis Tzirakis Eva-Maria Meßner Andreas König Alan Cowen Erik Cambria Björn W. Schuller
The MuSe 2023 is a set of shared tasks addressing three different
contemporary multimodal affect and sentiment analysis problems: In the Mimicked
Emotions Sub-Challenge (MuSe-Mimic), participants predict three continuous
emotion targets. This sub-challenge utilises the Hume-Vidmimic dataset
comprising of user-generated videos. For the Cross-Cultural Humour Detection
Sub-Challenge (MuSe-Humour), an extension of the Passau Spontaneous Football
Coach Humour (Passau-SFCH) dataset is provided. Participants predict the
presence of spontaneous humour in a cross-cultural setting. The Personalisation
Sub-Challenge (MuSe-Personalisation) is based on the Ulm-Trier Social Stress
Test (Ulm-TSST) dataset, featuring recordings of subjects in a stressed
situation. Here, arousal and valence signals are to be predicted, whereas parts
of the test labels are made available in order to facilitate personalisation.
MuSe 2023 seeks to bring together a broad audience from different research
communities such as audio-visual emotion recognition, natural language
processing, signal processing, and health informatics. In this baseline paper,
we introduce the datasets, sub-challenges, and provided feature sets. As a
competitive baseline system, a Gated Recurrent Unit (GRU)-Recurrent Neural
Network (RNN) is employed. On the respective sub-challenges' test datasets, it
achieves a mean (across three continuous intensity targets) Pearson's
Correlation Coefficient of .4727 for MuSe-Mimic, an Area Under the Curve (AUC)
value of .8310 for MuSe-Humor and Concordance Correlation Coefficient (CCC)
values of .7482 for arousal and .7827 for valence in the MuSe-Personalisation
sub-challenge.