The ACII 2022 Affective Vocal Bursts Workshop & Competition

• Published in: 2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) pp. 1 - 5
• Main Authors: Baird, Alice, Tzirakis, Panagiotis, Brooks, Jeffrey A., Gregory, Chris B., Schuller, Bjorn, Batliner, Anton, Keltner, Dacher, Cowen, Alan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.10.2022
• Subjects: Affective computing; Conferences; Correlation coefficient; Cultural aspects; Deep learning; emotional expression; machine learning; multi-label; Task analysis; Training; vocal bursts
• DOI: 10.1109/ACIIW57231.2022.10086002

The ACII Affective Vocal Bursts Workshop & Com-petition is focused on understanding multiple affective dimensions of vocal bursts: laughs, gasps, cries, screams, and many other non-linguistic vocalizations central to the expression of emotion and to human communication more generally. This year's com-petition comprises four tracks using a large-scale and in-the-wild dataset of 59,299 vocalizations from 1,702 speakers. The first, the A- VB-HIGH task, requires competition participants to perform a multi-label regression on a novel model for emotion, utilizing ten classes of richly annotated emotional expression intensities, including; Awe, Fear, and Surprise. The second, the A-VB-Two task, utilizes the more conventional 2-dimensional model for emotion, arousal, and valence. The third, the A- VB-CULTURE task, requires participants to explore the cultural aspects of the dataset, training native-country dependent models. Finally, for the fourth task, A - VB - TYPE, participants should recognize the type of vocal burst (e.g., laughter, cry, grunt) as an 8-class classification. This paper describes the four tracks and baseline systems, which use state-of-the-art machine learning methods. The baseline performance for each track is obtained by utilizing an end-to-end deep learning model and is as follows: for A- VB-HIGH, a mean (over the 10-dimensions) Concordance Correlation Coefficient (CCC) of 0.5687 CCC is obtained; for A- VB- Two, a mean (over the 2-dimensions) CCC of 0.5084 is obtained; for A-VB − CULTURE, a mean CCC from the four cultures of 0.4401 is obtained; and for A-VB-TYPE, the baseline Unweighted Average Recall (UAR) from the 8-classes is 0.4172 UAR.