论文标题
跨域方法，可从二元音频 - 视觉 - 偶像信号中持续印象识别
A Cross-Domain Approach for Continuous Impression Recognition from Dyadic Audio-Visual-Physio Signals
论文作者
论文摘要
我们对他人的印象不仅取决于我们说的话，而且在很大程度上取决于我们的说法。作为情感计算和社会信号处理的子分支，印象识别在人类对话和口头对话系统中都至关重要。但是，大多数研究仅从发射极表示的信号中研究了印象，忽略了接收器的响应。在本文中，我们使用在二元印象数据集中提出的跨域架构执行印象识别。这种改进的体系结构利用了跨域的注意力和正则化。跨域的注意力由内部和注意力间机制组成，分别捕获内部和域间相关性。跨域的正则化包括知识蒸馏和相似性增强损失，从而增强了发射极和接收器之间的特征连接。实验评估验证了我们方法的有效性。我们的方法在能力维度上实现了0.770的一致性相关系数，在温暖维度上达到了0.748。
The impression we make on others depends not only on what we say, but also, to a large extent, on how we say it. As a sub-branch of affective computing and social signal processing, impression recognition has proven critical in both human-human conversations and spoken dialogue systems. However, most research has studied impressions only from the signals expressed by the emitter, ignoring the response from the receiver. In this paper, we perform impression recognition using a proposed cross-domain architecture on the dyadic IMPRESSION dataset. This improved architecture makes use of cross-domain attention and regularization. The cross-domain attention consists of intra- and inter-attention mechanisms, which capture intra- and inter-domain relatedness, respectively. The cross-domain regularization includes knowledge distillation and similarity enhancement losses, which strengthen the feature connections between the emitter and receiver. The experimental evaluation verified the effectiveness of our approach. Our approach achieved a concordance correlation coefficient of 0.770 in competence dimension and 0.748 in warmth dimension.
