Deep Emotion Recognition Based On Audio-Visual Correlation

Human emotion recognition is studied by means of unimodal channels over the last decade. However, efforts continue to answer tempting questions about how variant modalities can complement each other. This study proposes a multimodal approach using three-dimensional (3D) convolutional neural networks (CNNs) to model human emotion through a modality-referenced system while investigating the solution to such questions. The proposed modality-referenced system selects the input data based on one of the modalities regarded as reference or master. The other modality which is referred to as a slave simply adjusts or attunes itself with the master in the temporal domain. In this context, the authors developed three multimodal emotion recognition system, namely, video-referenced system, audio-referenced system, and the audio-visual-referenced system to explore the congruence impact of audio and video modalities on each other. Two pipelines of 3D CNN architectures are employed where k-means clustering is used in the master pipeline and the slave pipeline adapts itself in a temporal sense. The outputs of the two pipelines are fused to improve recognition performance. In addition, canonical correlation analysis and t-distributed stochastic neighbour embedding is used validating the experiments. Results show that temporal alignment of the data between two modalities improves the recognition performance significantly.