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Estimating cognitive load from speech gathered in a complex real-life training exercise

机译：在复杂的真实培训锻炼中估算来自语音的认知负荷

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摘要

Speech-enabled applications are becoming prevalent, providing opportunities for real-time detection of speaker characteristics. Estimation of cognitive load from speech is one type of speaker characteristic that can provide insight into the human state in complex, highly dynamic human-machine teaming scenarios and be used to adapt interaction with the user to their current cognitive state. Cognitive load estimation from speech experiments are typically performed on speech gathered in laboratory settings. By contrast, this research is performed on a real life dataset that was not created for the purpose of cognitive load assessment. Speech was extracted from recordings of a military simulation exercise in which air battle managers communicated with pilots flying simulated aircraft. This paper assesses whether cognitive load can be estimated from speech self-labelled by exercise participants and collected in a realistic setting, and examines how well cognitive load estimation methods translate from the laboratory setting to the real-world. Analysis suggests that participants' self-assessment of workload at periodic intervals can be used to label speech to create 2-class cognitive load classifiers. The analysis also shows that including some target speaker speech in speaker independent training data results in higher classification accuracy than when classifiers are built solely from speaker dependent data.
机译：启用语音的应用程序普遍存在，提供了对扬声器特性的实时检测的机会。估计来自语音的认知负载是一种类型的扬声器特性，可以在复杂的高度动态人机组合场景中提供对人类状态的洞察，并用于使与用户的交互适应其当前的认知状态。语音实验中的认知负载估计通常在实验室设置中收集的语音上进行。相比之下，该研究是对未创建的真实生活数据集以认知负载评估而创建的。从军事模拟运动的录音中提取了演讲，其中航空战士与飞行模拟飞机的飞行员沟通。本文评估了认知负荷是否可以通过运动参与者自我标签的语音估计，并在现实环境中收集，并检查认知负载估计方法如何从实验室设置转换为现实世界。分析表明，参与者以周期性间隔的工作负载自我评估可用于标记语音以创建2级认知负载分类器。分析还显示，包括扬声器独立培训数据中的一些目标扬声器语音导致比分类器完全从扬声器相关数据建立时的分类准确性更高。

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