The 4th Multimodal Sentiment Analysis Challenge (MuSe) focuses on Multimodal Prediction of Mimicked Emotions, Cross-Cultural Humour, and Personalised Recognition of Affects. The workshop takes place in conjunction with ACM Multimedia'23. We provide three datasets as part of the challenge: (i) The Hume-Vidmimic dataset which offers 30+ hours of expressive behaviour data from 557 participants. It involves mimicking and rating emotions: Approval, Disappointment, and Uncertainty. This multimodal resource is valuable for studying human emotional expressions. (ii) The 2023 edition of the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset comprises German football press conference recordings within the training set, while videos of English football press conferences are included in the unseen test set. This unique configuration offers a cross-cultural evaluation environment for humour recognition. (iii) The Ulm-Trier Social Stress Test (Ulm-TSST) dataset contains recordings of subjects under stress. It involves arousal and valence signals, with some test labels provided to aid personalisation. Based on these datasets, we formulate three multimodal affective computing challenges: (1) Mimicked Emotions Sub-Challenge (MuSe-Mimic) for categorical emotion prediction, (2) Cross-Cultural Humour Detection Sub-Challenge (MuSe-Humour) for cross-cultural humour detection, and (3) Personalisation Sub-Challenge (MuSe-Personalisation) for personalised dimensional emotion recognition. In this summary, we outline the challenge's motivation, participation guidelines, conditions, and results.

The 3rd Multimodal Sentiment Analysis Challenge (MuSe) focuses on multimodal affective computing. The workshop is held in conjunction with ACM Multimedia'22. Three datasets are provided as part of the challenge: (i) the Passau Spontaneous Football Coach Humor (Passau-SFCH) dataset which contains humour-tagged audio-visual data of German football coaches, (ii) the Hume-Reaction dataset, which contains annotations on how people respond to emotional stimuli in terms of seven different emotional expression intensities, and (iii) the Ulm-Trier Social Stress Test (Ulm-TSST) dataset, which consists of audio-visual recordings labelled with continuous emotion values of individuals in stressful circumstances. Based on these datasets three affective computing challenges are defined: 1) Humor Detection Sub-Challenge (MuSe-Humor), for spontaneous humour recognition, 2) Emotional Reactions Sub-Challenge (MuSe-Reaction), for prediction of seven fine-grained in-the-wild' emotions, and 3) Emotional Stress Sub-Challenge (MuSe-Stress), for continuous prediction of stressed emotion values. In this summary, we describe the motivation behind the challenge, participation and its conditions, as well as the outcomes. The complete MuSe'22 workshop proceedings are available at: https://dl.acm.org/doi/proceedings/10.1145/3551876

The 2nd Multimodal Sentiment Analysis (MuSe) 2021 Challenge-based Workshop is held in conjunction with ACM Multimedia'21. Two datasets are provided as part of the challenge. Firstly, the MuSe-CaR dataset, which focuses on user-generated, emotional vehicle reviews from YouTube, and secondly, the novel Ulm-Trier Social Stress (Ulm-TSST) dataset, which shows people in stressful circumstances. Participants are faced with four sub-challenges: predicting arousal and valence in a time- and value-continuous manner on a) MuSe-CaR (MuSe-Wilder) and b) Ulm-TSST (MuSe-Stress); c) predicting unsupervised created emotion classes on MuSe-CaR (MuSe-Sent); d) predicting a fusion of human-annotated arousal and measured galvanic skin response also as a continuous target on Ulm-TSST (MuSe-Physio). In this summary, we describe the motivation, the sub-challenges, the challenge conditions, the participation, and the most successful approaches.

The utilization of mobile technology in the field of medicine and healthcare has become a decisive aspect. The entire field is denoted as mobile health (mHealth). For mHealth, the development and use of mobile applications are crucial. The purposes and goals of mHealth apps, in turn, are manifold. As a consequence, a plethora of mHealth apps can be found in the app stores. Interestingly, for patients, users, and health care providers that consider to use mHealth apps one aspect has been less pursued so far: Systematic and standardized ways that help about the quality of an app or its medical evidence are mainly missing. The Mobile App Rating Scale (MARS) is a standardized instrument that aims at the systematic and comparable evaluation of the quality of mobile health apps as well as categorizing their goals and functions. It comprises 23 items, which are utilized to calculate a rating scale. Having MARS in mind, a database was developed that is called Mobile Health App Database (MHAD). The latter offers technical features to systematically utilize the MARS for researchers as well as clinicians and end-users that (i) want to evaluate apps as well as (ii) want an interactive and easy-to-use web interface that shows the results of the rating procedure. MHAD comprises a rating platform that supports the conduction of MARS ratings and their release process. With the information platform, a web application was developed that prepares the data stored in the rating platform for being freely viewed and studied by users, patients, and health care providers. The goal of MHAD constitutes to be an open science repository that encourages researchers to release their MARS ratings to a broader audience. Such repositories become more and more important in many fields, especially in the field of mHealth.

The INTERSPEECH 2020 Computational Paralinguistics Challenge addresses three different problems for the first time in a research competition under well-defined conditions: In the Elderly Emotion Sub-Challenge, arousal and valence in the speech of elderly individuals have to be modelled as a 3-class problem; in the Breathing Sub-Challenge, breathing has to be assessed as a regression problem; and in the Mask Sub-Challenge, speech without and with a surgical mask has to be told apart.We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AUDEEP toolkit, and deep feature extraction from pre-trained CNNs using the DEEP SPECTRUM toolkit; in addition, we partially add deep end-to-end sequential modelling, and, for the first time in the challenge, linguistic analysis.

We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts. For PNs, we define emotion carriers as the speech or text segments that best explain the emotional state of the narrator. Such segments may span from single to multiple words, containing for example verb or noun phrases. Advanced automatic understanding of PNs requires not only the prediction of the narrator’s emotional state but also to identify which events (e.g. the loss of a relative or the visit of grandpa) or people (e.g. the old group of high school mates) carry the emotion manifested during the personal recollection. This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives. Compared to other text genres such as news and microblogs, spoken PNs are particularly challenging because a narrative is usually unstructured, involving multiple sub-events and characters as well as thoughts and associated emotions perceived by the narrator. In this work, we experiment with annotating emotion carriers in speech transcriptions from the Ulm State-of-Mind in Speech (USoMS) corpus, a dataset of PNs in German. We believe this resource could be used for experiments in the automatic extraction of emotion carriers from PN, a task that could provide further advancements in narrative understanding.

Automatic detection of sentiment and affect in personal narratives through word usage has the potential to assist in the automated detection of change in psychotherapy. Such a tool could, for instance, provide an efficient, objective measure of the time a person has been in a positive or negative state-of-mind. Towards this goal, we propose and develop a hierarchical attention model for the tasks of sentiment (positive and negative) and self-assessed affect detection in transcripts of personal narratives. We also perform a qualitative analysis of the word attentions learnt by our sentiment analysis model. In a key result, our attention model achieved an un-weighted average recall (UAR) of 91.0 % in a binary sentiment detection task on the test partition of the Ulm State-of-Mind in Speech (USoMS) corpus. We also achieved UARs of 73.7 % and 68.6 % in the 3-class tasks of arousal and valence detection respectively. Finally, our qualitative analysis associates colloquial reinforcements with positive sentiments, and uncertain phrasing with negative sentiments.

The effect of stress on the human body is substantial, potentially resulting in serious health implications.Furthermore, with modern stressors seemingly on the increase, there is an abundance of contributing factors which lead to a diagnosis of acute stress.However, observing biological stress reactions usually includes costly and time consuming sequential fluidbased samples to determine the degree of biological stress.On the contrary, a speech monitoring approach would allow for a non-invasive indication of stress.To evaluate the efficacy of the speech signal as a marker of stress, we explored, for the first time, the relationship between sequential cortisol samples and speech-based features.Utilising a novel corpus of 43 individuals undergoing a standardised Trier Social Stress Test (TSST), we extract a variety of feature sets and observe a correlation between speech and sequential cortisol measurements.For prediction of mean cortisol levels from speech, results show that for the entire TSST oral presentation, handcrafted COMPARE features achieve best results of 0.244 root mean square error [0 ;1] for the sample 20 minutes after the TSST.Correlation also increases at minute 20, with a Spearman's correlation coefficient of 0.421, and Cohen's d of 0.883 between the baseline and minute 20 cortisol predictions.

Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e.g. early diagnosis of mental diseases, supervision of disease course, etc.). In the Interspeech 2018 ComParE Self-Assessed Affect challenge, the task of valence prediction was framed as a three-class classification problem using 8 seconds fragments from individuals' narratives. As such, the task did not allow for exploring contextual information of the narratives. In this work, we investigate the intrinsic information from multiple narratives recounted by the same individual in order to predict their current state-of-mind. Furthermore, with generalizability in mind, we decided to focus our experiments exclusively on textual information as the public availability of audio narratives is limited compared to text. Our hypothesis is that context modeling might provide insights about emotion triggering concepts (e.g. events, people, places) mentioned in the narratives that are linked to an individual's state of mind. We explore multiple machine learning techniques to model narratives. We find that the models are able to capture inter-individual differences, leading to more accurate predictions of an individual's emotional state, as compared to single narratives.