Autism spectrum disorder (ASD) is a developmental disorder that influences the communication and social behavior of a person in a way that those in the spectrum have difficulty in perceiving other people's facial expressions, as well as presenting and communicating emotions and affect via their own faces and bodies. Some efforts have been made to predict and improve children with ASD's affect states in play therapy, a common method to improve children's social skills via play and games. However, many previous works only used pre-trained models on benchmark emotion datasets and failed to consider the distinction in emotion between typically developing children and children with autism. In this paper, we present an open-source two-stage multi-modal approach leveraging acoustic and visual cues to predict three main affect states of children with ASD's affect states (positive, negative, and neutral) in real-world play therapy scenarios, and achieved an overall accuracy of 72:40%. This work presents a novel way to combine human expertise and machine intelligence for ASD affect recognition by proposing a two-stage schema.

中文翻译：

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自闭症谱系障碍儿童的两阶段多模态情感分析框架

自闭症谱系障碍 (ASD) 是一种发育障碍，影响一个人的交流和社会行为，导致该谱系中的人难以感知他人的面部表情，也难以通过自己的脸表达和交流情绪和情感和身体。已经做出一些努力来预测和改善 ASD 儿童在游戏治疗中的情感状态，这是一种通过游戏和游戏提高儿童社交技能的常用方法。然而，许多先前的工作仅在基准情绪数据集上使用预先训练的模型，而未能考虑典型发育儿童和自闭症儿童之间的情绪差异。在本文中，我们提出了一种开源的两阶段多模态方法，利用声学和视觉线索来预测真实世界游戏治疗场景中 ASD 儿童的三种主要影响状态（积极、消极和中性），并取得了整体准确率为 72:40%。这项工作通过提出一个两阶段的模式，提出了一种将人类专业知识和机器智能结合起来进行 ASD 影响识别的新方法。