MuSe-Humor: Cross-Cultural Humor Detection Sub-Challenge (MuSe 2024) - Videos

<p><strong>Description</strong>: Predicting the presence of humor in football press conference recordings. Available modalities: audio and video, transcriptions. The dataset offers a cross-cultural setting: all press conferences in the training and development partition are held in German, and all videos in the test partition are taken from English Premier League press conferences.</p> <p><strong>Important</strong>: <strong>This package only contains the video recordings, please find labels, audio files, transcripts and features here</strong>: <a href="../records/11211840">https://zenodo.org/records/11211840</a></p> <p><strong>Labels</strong>: windows of 2 seconds are given a binary label, indicating the presence and absence of humor. At least 7 human annotators labeled each video.</p> <p><strong>Dataset</strong>: MuSe-Humor is based on the Passau-Spontaneous Football Coach Humor (Passau-SFCH) dataset (<a href="https://arxiv.org/pdf/2209.14272.pdf">paper</a>). It includes press conference recordings of 10 different German Bundesliga football coaches collected in 2017. Moreover, for the MuSe 2024 challenge, recordings of 6 Premier League coaches have been added. The data provided here only includes segments in which the respective coach is speaking. The training data set contains recordings of 7 German-speaking coaches, development data consists of videos of 3 German-speaking coaches, and the test data set comprises recordings of 6 English-speaking coaches (one native speaker).</p> <p><strong>General</strong>: The&nbsp;<a href="http://muse-challenge.org/">5th Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2024</a> addresses research questions that are of interest to affective computing, machine learning and multimodal signal processing communities and encourages a fusion of their disciplines. The goal of the MuSe workshop and challenge is to gain new insights into the merits of each of the core modalities and to serve as a stimulating environment for the development and evaluation of multimodal affect recognition approaches.</p>