论文标题
通过组合半监督和监督学习来解决多模式用户状态识别中的数据稀缺性
Addressing Data Scarcity in Multimodal User State Recognition by Combining Semi-Supervised and Supervised Learning
论文作者
论文摘要
检测人类用户的心理状态对于开发合作和智能机器人至关重要，因为它使机器人能够了解用户的意图和欲望。尽管它们的重要性，但很难获得大量的高质量数据来训练自动识别算法，因为收集和标记此类数据所需的时间和精力是过高的。在本文中，我们提出了一种多模式学习方法，用于在人类机器人相互作用环境中使用少量手动注释的数据在人类机器人相互作用环境中检测疾病和混乱状态。我们通过进行人类机器人相互作用研究来收集数据集，并为我们的机器学习方法开发新的预处理管道。通过将半监督和监督的体系结构相结合，我们能够达到平均F1分数为81.1 \％，用于删除/一致性检测以及少量标记的数据和大量未标记的数据集，同时增加了模型的稳健性。
Detecting mental states of human users is crucial for the development of cooperative and intelligent robots, as it enables the robot to understand the user's intentions and desires. Despite their importance, it is difficult to obtain a large amount of high quality data for training automatic recognition algorithms as the time and effort required to collect and label such data is prohibitively high. In this paper we present a multimodal machine learning approach for detecting dis-/agreement and confusion states in a human-robot interaction environment, using just a small amount of manually annotated data. We collect a data set by conducting a human-robot interaction study and develop a novel preprocessing pipeline for our machine learning approach. By combining semi-supervised and supervised architectures, we are able to achieve an average F1-score of 81.1\% for dis-/agreement detection with a small amount of labeled data and a large unlabeled data set, while simultaneously increasing the robustness of the model compared to the supervised approach.
