论文标题
一种用于衡量二元对话中声乐夹带的计算方法
A Context-Aware Computational Approach for Measuring Vocal Entrainment in Dyadic Conversations
论文作者
论文摘要
人声夹带是人类互动中的一种社会适应机制,知识可以为个人的认知行为特征提供有用的见解。我们提出了一种衡量二元对话中声乐夹带的情境感知方法。我们使用构象异构体(卷积网络和变压器的组合)来捕获短期和长期对话环境,以模拟不同领域的相互作用中的夹带模式。具体而言,我们使用跨主体注意层来学习二元对话中的内部和人际信号。我们首先根据分类实验验证提出的方法,以区分真实(一致)和假(不一致/洗牌)对话。关于涉及自闭症谱系障碍个体的相互作用的实验结果还显示了引入的夹带措施与与症状相关的临床评分之间的统计学意义相关的证据,包括性别和年龄组。
Vocal entrainment is a social adaptation mechanism in human interaction, knowledge of which can offer useful insights to an individual's cognitive-behavioral characteristics. We propose a context-aware approach for measuring vocal entrainment in dyadic conversations. We use conformers(a combination of convolutional network and transformer) for capturing both short-term and long-term conversational context to model entrainment patterns in interactions across different domains. Specifically we use cross-subject attention layers to learn intra- as well as inter-personal signals from dyadic conversations. We first validate the proposed method based on classification experiments to distinguish between real(consistent) and fake(inconsistent/shuffled) conversations. Experimental results on interactions involving individuals with Autism Spectrum Disorder also show evidence of a statistically-significant association between the introduced entrainment measure and clinical scores relevant to symptoms, including across gender and age groups.