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Audiovisual emotion recognition using ANOVA feature selection method and multi-classifier neural networks

机译：基于ANOVA特征选择方法和多分类器神经网络的视听情感识别

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摘要

To make human-computer interaction more naturally and friendly, computers must enjoy the ability to understand human's affective states the same way as human does. There are many modals such as face, body gesture and speech that people use to express their feelings. In this study, we simulate human perception of emotion through combining emotion-related information using facial expression and speech. Speech emotion recognition system is based on prosody features, mel-frequency cepstral coefficients (a representation of the short-term power spectrum of a sound) and facial expression recognition based on integrated time motion image and quantized image matrix, which can be seen as an extension to temporal templates. Experimental results showed that using the hybrid features and decision-level fusion improves the outcome of unimodal systems. This method can improve the recognition rate by about 15% with respect to the speech unimodal system and by about 30% with respect to the facial expression system. By using the proposed multi-classifier system that is an improved hybrid system, recognition rate would increase up to 7.5% over the hybrid features and decision-level fusion with RBF, up to 22.7% over the speech-based system and up to 38% over the facial expression-based system.
机译：为了使人机交互更加自然和友好，计算机必须像人一样具有理解人的情感状态的能力。人们使用多种模式来表达自己的感受，例如面部表情，身体手势和言语。在这项研究中，我们通过结合使用面部表情和语音的与情感相关的信息来模拟人类对情感的感知。语音情感识别系统基于韵律特征，梅尔频率倒谱系数（声音的短期功率谱的表示）和基于集成时间运动图像和量化图像矩阵的面部表情识别，可以看作是时态模板的扩展。实验结果表明，使用混合特征和决策级融合可以改善单峰系统的结果。此方法相对于语音单峰系统，可将识别率提高约15％，相对于面部表情系统，可将识别率提高约30％。通过使用提议的多分类器系统（一种改进的混合系统），识别率将比混合功能和决策级与RBF的融合提高高达7.5％，与基于语音的系统相比提高了22.7％，达38％在基于面部表情的系统上。

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