Automatic Emotion Detection (ED) aims to build systems to identify users' emotions automatically. This field has the potential to enhance HCI, creating an individualised experience for the user. However, ED systems tend to perform poorly on people with Autism Spectrum Disorder (ASD). Hence, the need to create ED systems tailored to how people with autism express emotions. Previous works have created ED systems tailored for children with ASD but did not share the resulting dataset. Sharing annotated datasets is essential to enable the development of more advanced computer models for ED within the research community. In this paper, we describe our experience establishing a process to create a multimodal annotated dataset featuring children with a level 1 diagnosis of autism. In addition, we introduce CALMED (Children, Autism, Multimodal, Emotion, Detection), the resulting multimodal emotion detection dataset featuring children with autism aged 8-12. CALMED includes audio and video features extracted from recording files of study sessions with participants, together with annotations provided by their parents into four target classes. The generated dataset includes a total of 57,012 examples, with each example representing a time window of 200ms (0.2s). Our experience and methods described here, together with the dataset shared, aim to contribute to future research applications of affective computing in ASD, which has the potential to create systems to improve the lives of people with ASD.

翻译：摘要：自动情感检测（ED）旨在构建能够自动识别用户情感的系统。该领域有潜力增强人机交互，为用户创造个性化体验。然而，情感检测系统在孤独症谱系障碍（ASD）患者中表现往往不佳。因此，有必要创建针对孤独症患者情感表达方式定制的情感检测系统。以往研究已开发出针对ASD儿童的情感检测系统，但未共享其生成的数据集。共享标注数据集对于推动研究社区中更先进的情感检测计算机模型的发展至关重要。本文描述了我们在建立针对一级ASD诊断儿童的多模态标注数据集创建流程中的经验。此外，我们介绍了CALMED（儿童、孤独症、多模态、情感、检测）——一个面向8-12岁ASD儿童的最终多模态情感检测数据集。该数据集包含从参与者研究会话记录文件中提取的音频和视频特征，以及由家长标注的四个目标类别。生成的数据集共包含57,012个样本，每个样本代表200毫秒（0.2秒）的时间窗口。本文所述的经验与方法，以及共享的数据集，旨在为未来ASD领域的情感计算研究应用做出贡献，该领域有潜力构建改善ASD患者生活的系统。