ACII 2022情感声乐爆发研讨会和比赛：了解情感表达的一种批判性研究不足的形式
The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression
摘要：
ACII情感发声研讨会和比赛的重点是理解发声的多个情感维度：笑，喘息，哭泣，尖叫和许多其他非语言发声对情感表达和人类交流更普遍。今年的比赛包括四个曲目，使用了来自1,702名演讲者的59,299个发声的大规模野外数据集。第一个是A-VB-High任务，要求参赛者对一个新的情感模型进行多标签回归，利用十类丰富注释的情感表达强度，包括：敬畏，恐惧和惊喜。第二个任务是A-VB-Two任务，它使用了更传统的二维模型来描述情绪、唤醒和效价。第三个是A-VB-Culture任务，要求参与者探索数据集的文化方面，训练依赖于本国的模型。最后，对于第四个任务，A-VB-Type，参与者应该识别声音爆发的类型（例如，笑，哭，咕哝）作为8级分类。本文描述了四个轨道和基线系统，这些系统使用最先进的机器学习方法。每个赛道的基线性能是通过利用端到端深度学习模型获得的，如下所示：（在10个维度上）获得0.5687 CCC的一致性相关系数（CCC）;对于A-VB-Two，平均值对于A-VB-Culture，获得来自四种培养物的0.4401的平均CCC;而对于A-VB-Type，来自8类的基线未加权平均召回（UAR）为0.4172 UAR。
英文摘要：
The ACII Affective Vocal Bursts Workshop & Competition 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 competition 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
