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A Review and Meta-Analysis of Multimodal Affect Detection Systems

机译：多模式情感检测系统的回顾与荟萃分析

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摘要

Affect detection is an important pattern recognition problem that has inspired researchers from several areas. The field is in need of a systematic review due to the recent influx of Multimodal (MM) affect detection systems that differ in several respects and sometimes yield incompatible results. This article provides such a survey via a quantitative review and meta-analysis of 90 peer-reviewed MM systems. The review indicated that the state of the art mainly consists of person-dependent models (62.2% of systems) that fuse audio and visual (55.6%) information to detect acted (52.2%) expressions of basic emotions and simple dimensions of arousal and valence (64.5%) with feature-(38.9%) and decision-level (35.6%) fusion techniques. However, there were also person-independent systems that considered additional modalities to detect nonbasic emotions and complex dimensions using model-level fusion techniques. The meta-analysis revealed that MM systems were consistently (85% of systems) more accurate than their best unimodal counterparts, with an average improvement of 9.83% (median of 6.60%). However, improvements were three times lower when systems were trained on natural (4.59%) versus acted data (12.7%). Importantly, MM accuracy could be accurately predicted (cross-validated R-2 of 0.803) from unimodal accuracies and two system-level factors. Theoretical and applied implications and recommendations are discussed.
机译：情感检测是一个重要的模式识别问题，启发了来自多个领域的研究人员。由于最近涌入的多峰（MM）影响检测系统在几个方面有所不同，有时会产生不兼容的结果，因此该领域需要系统地审查。本文通过对90个同行评审的MM系统进行定量审查和荟萃分析，提供了这样的调查。审查表明，现有技术主要由依赖于人的模型（占系统的62.2％）组成，这些模型融合了视听信息（55.6％）来检测基本情感的行为表达（52.2％）以及唤醒和价的简单维度（64.5％）具有特征（38.9％）和决策级（35.6％）融合技术。但是，也存在一些独立于人的系统，这些系统考虑了使用模型级融合技术检测非基本情绪和复杂维度的其他方式。荟萃分析显示，MM系统始终比最佳单峰系统准确（占系统的85％），平均提高了9.83％（中位数为6.60％）。但是，对系统进行自然数据训练（4.59％）要比对实际数据进行训练（12.7％）要低三倍。重要的是，可以从单峰精度和两个系统级因素准确预测MM精度（交叉验证的R-2为0.803）。理论和应用意义和建议进行了讨论。

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