| 摘要: |
| 自动情感识别是一个非常具有挑战性的课题，并且有着广泛的应用价值.探讨了在多文化场景下的多模态情感识别问题.从语音声学和面部表情等模态分别提取了不同的情感特征，包括传统的手工定制特征和基于深度学习的特征，并通过多模态融合方法结合不同的模态，比较不同单模态特征和多模态特征融合的情感识别性能.在CHEAVD中文多模态情感数据集和AFEW英文多模态情感数据集进行实验，通过跨文化情感识别研究，验证了文化因素对于情感识别的重要影响，并提出3种训练策略提高在多文化场景下情感识别的性能，包括：分文化选择模型、多文化联合训练以及基于共同情感空间的多文化联合训练，其中，基于共同情感空间的多文化联合训练通过将文化影响与情感特征分离，在语音和多模态情感识别中均取得最好的识别效果. |
| 关键词: 情感识别 多文化场景 语音情感特征 面部表情特征 多模态融合 深度卷积神经网络 |
| DOI：10.13328/j.cnki.jos.005412 |
| 分类号: |
| 基金项目:国家重点研发计划（2016YFB1001200） |
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| Multimodal Emotion Recognition in Multi-Cultural Conditions |
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CHEN Shi-Zhe, WANG Shuai, JIN Qin
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School of Information, Renmin University of China, Beijing 100872, China
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| Abstract: |
| Automatic emotion recognition is a challenging task with a wide range of applications. This paper addresses the problem of emotion recognition in multi-cultural conditions. Different multi-modal features are extracted from audio and visual modalities, and the emotion recognition performance is compared between hand-crafted features and automatically learned features from deep neural networks. Multimodal feature fusion is also explored to combine different modalities. The CHEAVD Chinese multimodal emotion dataset and AFEW English multimodal emotion dataset are utilized to evaluate the proposed methods. The importance of the culture factor for emotion recognition through cross-culture emotion recognition is demonstrated, and then three different strategies, including selecting corresponding emotion model for different cultures, jointly training with multi-cultural datasets, and embedding features from multi-cultural datasets into the same emotion space, are developed to improve the emotion recognition performance in the multi-cultural environment. The embedding strategy separates the culture influence from original features and can generate more discriminative emotion features, resulting in best performance for acoustic and multimodal emotion recognition. |
| Key words: emotion recognition multi-cultural condition acoustic emotion feature facial expression feature multimodal fusion deepconvolutional neural networks |
