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首页> 外文期刊>IEEE transactions on multimedia >Dynamic Difficulty Awareness Training for Continuous Emotion Prediction
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Dynamic Difficulty Awareness Training for Continuous Emotion Prediction

机译：持续情绪预测的动态难度意识训练

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摘要

Time-continuous emotion prediction has become an increasingly compelling task in machine learning. Considerable efforts have been made to advance the performance of these systems. Nonetheless, the main focus has been the development of more sophisticated models and the incorporation of different expressive modalities (e.g., speech, face, and physiology). In this paper, motivated by the benefit of difficulty awareness in a human learning procedure, we propose a novel machine learning framework, namely, dynamic difficulty awareness training (DDAT), which sheds fresh light on the research-directly exploiting the difficulties in learning to boost the machine learning process. The DDAT framework consists of two stages: information retrieval and information exploitation. In the first stage, we make use of the reconstruction error of input features or the annotation uncertainty to estimate the difficulty of learning specific information. The obtained difficulty level is then used in tandem with original features to update the model input in a second learning stage with the expectation that the model can learn to focus on high difficulty regions of the learning process. We perform extensive experiments on a benchmark database REmote COLlaborative and affective to evaluate the effectiveness of the proposed framework. The experimental results show that our approach outperforms related baselines as well as other well-established time-continuous emotion prediction systems, which suggests that dynamically integrating the difficulty information for neural networks can help enhance the learning process.
机译：时间连续情感预测已成为机器学习中越来越引人注目的任务。为了提高这些系统的性能已经做出了相当大的努力。尽管如此，主要焦点仍在开发更复杂的模型以及纳入不同的表达方式（例如语音，面部和生理学）。在本文中，出于在人类学习过程中困难意识的好处，我们提出了一种新颖的机器学习框架，即动态困难意识训练（DDAT），它为直接研究学习中的困难提供了新的思路。促进机器学习过程。 DDAT框架包括两个阶段：信息检索和信息开发。在第一阶段，我们利用输入特征的重构误差或注释不确定性来估计学习特定信息的难度。然后，将获得的难度级别与原始功能结合使用，以在第二个学习阶段中更新模型输入，以期望模型可以学习专注于学习过程中的高难度区域。我们在基准数据库上进行了广泛的实验，以远程方式对情感和情感进行评估，以评估所提出框架的有效性。实验结果表明，我们的方法优于相关的基线以及其他完善的时间连续情感预测系统，这表明动态集成神经网络的难度信息可以帮助增强学习过程。

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  • 来源
    《IEEE transactions on multimedia》 |2019年第5期|1289-1301|共13页
  • 作者

    Zhang Zixing; Han Jing; Coutinho Eduardo; Schuller Bjorn;

  • 作者单位

    Imperial Coll London, Grp Language Audio & Mus, London SW7 2AZ, England;

    Univ Augsburg, ZD B Chair Embedded Intelligence Hlth Care & Well, D-86159 Augsburg, Germany;

    Univ Augsburg, ZD B Chair Embedded Intelligence Hlth Care & Well, D-86159 Augsburg, Germany|Univ Liverpool, Dept Mus, Liverpool L69 3BX1, Merseyside, England;

    Imperial Coll London, Grp Language Audio & Mus, London SW7 2AZ, England|Univ Augsburg, ZD B Chair Embedded Intelligence Hlth Care & Well, D-86159 Augsburg, Germany;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Emotion prediction; difficulty awareness learning; dynamic learning;

    机译：情绪预测;难以理解学习;动态学习;

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