使用玻尔兹曼拉链的视听情感识别

Kun Lu, Yunde Jia
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引用次数: 10

摘要

提出了一种新的自动视听情感识别方法。音频和视觉通道为人类情绪状态识别提供了互补的信息,我们利用玻尔兹曼拉链作为模型级融合来学习不同模式之间的内在相关性。我们提取了具有不同时间尺度的有效音频和视觉特征流,并分别将其送入两条玻尔兹曼链。两条链的隐藏单元是相互连接的。二阶方法应用于玻尔兹曼拉链,以加快学习和修剪过程。在《绿野仙踪》场景中收集的视听情感数据的实验结果表明,我们的方法是有前途的,并且优于单模态HMM和传统的耦合HMM方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Audio-visual emotion recognition using Boltzmann Zippers
This paper presents a novel approach for automatic audio-visual emotion recognition. The audio and visual channels provide complementary information for human emotional states recognition, and we utilize Boltzmann Zippers as model-level fusion to learn intrinsic correlations between the different modalities. We extract effective audio and visual feature streams with different time scales and feed them to two Boltzmann chains respectively. The hidden units of two chains are interconnected. Second-order methods are applied to Boltzmann Zippers to speed up learning and pruning process. Experimental results on audio-visual emotion data collected in Wizard of Oz scenarios demonstrate our approach is promising and outperforms single modal HMM and conventional coupled HMM methods.
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