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Multimodal Emotion Recognition using Deep Continuous Conditional Recurrent Neural Fields

机译：使用深度连续条件递归神经场的多模态情绪识别

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A deep Continuous Conditional Recurrent Neural Fields (CCRNF) framework is presented in this paper to model dimensional emotion from multiple input features. The deep architecture is effected by stacking multiple gated recurrent neural networks to model complex, non-linear relationships across time and space. The effect of increasing layer depth is studied through a comparative performance analysis and a visual depiction of the gate activations. The resulting visual analysis provides insight into the flow of information across time and multiple layers. The paper further investigates the use of model uncertainty as captured in the Gaussian distribution of the model, and explores the use of inverse variances in the fusion of model decisions. This latter study serves as an initial discussion in quantifying model and prediction confidence in continuous conditional random fields.
机译：本文提出了一个深层次的连续条件递归神经场（CCRNF）框架，以对来自多个输入特征的维数情感进行建模。通过堆叠多个门控递归神经网络以对跨时间和空间的复杂非线性关系进行建模，可以实现深度结构。通过比较性能分析和可视化的栅极激活，研究了增加层深度的影响。最终的可视化分析可洞察跨时间和多层的信息流。本文进一步研究了在模型的高斯分布中捕获的模型不确定性的使用，并探讨了在模型决策融合中逆方差的使用。后面的研究作为对连续条件随机场中模型的量化和预测置信度的初步讨论。

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