[Research on bimodal emotion recognition algorithm based on multi-branch bidirectional multi-scale time perception].

Q4 Medicine
Peiyun Xue, Sibin Wang, Jing Bai, Yan Qiang
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引用次数: 0

Abstract

Emotion can reflect the psychological and physiological health of human beings, and the main expression of human emotion is voice and facial expression. How to extract and effectively integrate the two modes of emotion information is one of the main challenges faced by emotion recognition. In this paper, a multi-branch bidirectional multi-scale time perception model is proposed, which can detect the forward and reverse speech Mel-frequency spectrum coefficients in the time dimension. At the same time, the model uses causal convolution to obtain temporal correlation information between different scale features, and assigns attention maps to them according to the information, so as to obtain multi-scale fusion of speech emotion features. Secondly, this paper proposes a two-modal feature dynamic fusion algorithm, which combines the advantages of AlexNet and uses overlapping maximum pooling layers to obtain richer fusion features from different modal feature mosaic matrices. Experimental results show that the accuracy of the multi-branch bidirectional multi-scale time sensing dual-modal emotion recognition model proposed in this paper reaches 97.67% and 90.14% respectively on the two public audio and video emotion data sets, which is superior to other common methods, indicating that the proposed emotion recognition model can effectively capture emotion feature information and improve the accuracy of emotion recognition.

基于多分支双向多尺度时间感知的双峰情绪识别算法研究
情感可以反映人的心理和生理健康状况,人类情感的主要表现形式是声音和面部表情。如何提取并有效整合两种模式的情感信息是情感识别面临的主要挑战之一。本文提出了一种多分支双向多尺度时间感知模型,该模型可以在时间维度上检测语音的正反向mel频谱系数。同时,该模型利用因果卷积获取不同尺度特征之间的时间相关信息,并根据这些信息为其分配注意图,从而获得语音情感特征的多尺度融合。其次,结合AlexNet的优点,提出了一种双模态特征动态融合算法,利用重叠最大池化层从不同模态特征拼接矩阵中获得更丰富的融合特征。实验结果表明,本文提出的多分支双向多尺度时感双模态情绪识别模型在两种公开的音频和视频情绪数据集上的准确率分别达到97.67%和90.14%,优于其他常用方法,表明本文提出的情绪识别模型能够有效地捕获情绪特征信息,提高了情绪识别的准确率。
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来源期刊
生物医学工程学杂志
生物医学工程学杂志 Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
4868
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