Multi-Modal Emotion Recognition by Fusing Correlation Features of Speech-Visual

To effectively fuse speech and visual features, this letter proposes a multi-modal emotion recognition method by fusing correlation features of speech-visual. Firstly, speech and visual features are extracted by two-dimensional convolutional neural network (2D-CNN) and three-dimensional convolutiona...

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Veröffentlicht in:	IEEE signal processing letters 2021, Vol.28, p.533-537
Hauptverfasser:	Guanghui, Chen, Xiaoping, Zeng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Correlation Correlation analysis Emotion recognition emotion recongnition Emotions Feature extraction Mel frequency cepstral coefficient multi-modal Neural networks Speech Speech processing Speech recognition speech-visual Support vector machines Visualization
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