Detecting emotionally expressive nonverbal vocalizations is essential to developing technologies that can converse fluently with humans. The affective computing community has largely focused on understanding the intonation of emotional speech and language. However, advances in the study of vocal emotional behavior suggest that emotions may be more readily conveyed not by speech but by nonverbal vocalizations such as laughs, sighs, shrieks, and grunts – vocalizations that often occur in lieu of speech. The task of detecting such emotional vocalizations has been largely overlooked by researchers, likely due to the limited availability of data capturing a sufficiently wide variety of vocalizations. Most studies in the literature focus on detecting laughter or cries. In this paper, we present the first, to the best of our knowledge, nonverbal vocalization detection model trained to detect as many as 67 types of emotional vocalizations. For our purposes, we use the large-scale and in-the-wild HUME-VB dataset that provides more than 156 h of data. We thoroughly investigate the use of pre-trained audio transformer models, such as Wav2Vec2 and Whisper, and provide useful insights for the task at hand using different types of noise signals.

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 ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the Emotion Share Sub-Challenge, a regression on speech has to be made; and in the Requests Sub-Challenges, requests and complaints need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComPaRE features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectRum toolkit; in addition, wav2vec2 models are used.

Federated learning mitigates the need to store user data in a central datastore for machine learning tasks, and is particularly beneficial when working with sensitive user data or tasks. Although successfully used for applications such as improving keyboard query suggestions, it is not studied systematically for modeling affective computing tasks which are often laden with subjective labels and high variability across individuals/raters or even by the same participant. In this paper, we study the federated averaging algorithm FedAvg to model self-reported emotional experience and perception labels on a variety of speech, video and text datasets. We identify two learning paradigms that commonly arise in affective computing tasks: modeling of self-reports (user-as-client), and modeling perceptual judgments such as labeling sentiment of online comments (rater-as-client). In the user-as-client setting, we show that FedAvg generally performs on-par with a non-federated model in classifying self-reports. In the rater-as-client setting, FedAvg consistently performed poorer than its non-federated counterpart. We found that the performance of FedAvg degraded for classes where the inter-rater agreement was moderate to low. To address this finding, we propose an algorithm FedRater that learns client-specific label distributions in federated settings. Our experimental results show that FedRater not only improves the overall classification performance compared to FedAvg but also provides insights for estimating proxies of inter-rater agreement in distributed settings.

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

Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a fine-grained typology, adaptable to multiple downstream tasks. We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. We demonstrate the high quality of the annotations via Principal Preserved Component Analysis. We conduct transfer learning experiments with existing emotion benchmarks to show that our dataset generalizes well to other domains and different emotion taxonomies. Our BERT-based model achieves an average F1-score of .46 across our proposed taxonomy, leaving much room for improvement.