Analyzing individual emotions during group conversation is crucial in
developing intelligent agents capable of natural human-machine interaction.
While reliable emotion recognition techniques depend on different modalities
(text, audio, video), the inherent heterogeneity between these modalities and
the dynamic cross-modal interactions influenced by an individual’s unique
behavioral patterns make the task of emotion recognition very challenging. This
difficulty is compounded in group settings, where the emotion and its temporal
evolution are not only influenced by the individual but also by external
contexts like audience reaction and context of the ongoing conversation. To
meet this challenge, we propose a Multimodal Attention Network that captures
cross-modal interactions at various levels of spatial abstraction by jointly
learning its interactive bunch of mode-specific Peripheral and Central
networks. The proposed MAN injects cross-modal attention via its Peripheral
key-value pairs within each layer of a mode-specific Central query network. The
resulting cross-attended mode-specific descriptors are then combined using an
Adaptive Fusion technique that enables the model to integrate the
discriminative and complementary mode-specific data patterns within an
instance-specific multimodal descriptor. Given a dialogue represented by a
sequence of utterances, the proposed AMuSE model condenses both spatial and
temporal features into two dense descriptors: speaker-level and
utterance-level. This helps not only in delivering better classification
performance (3-5% improvement in Weighted-F1 and 5-7% improvement in Accuracy)
in large-scale public datasets but also helps the users in understanding the
reasoning behind each emotion prediction made by the model via its Multimodal
Explainability Visualization module.

Analyzing individual emotions during group conversations is a crucial aspect of developing intelligent agents capable of natural human-machine interaction. This article highlights the challenges in emotion recognition techniques due to the heterogeneity between different modalities such as text, audio, and video. The dynamics of cross-modal interactions influenced by an individual’s behavioral patterns further complicates the task.

In the field of multimedia information systems, understanding and recognizing emotions in various modalities is essential for creating effective user interfaces and personalized experiences. By developing a Multimodal Attention Network (MAN), the researchers propose a solution to capture cross-modal interactions at different levels of spatial abstraction. This multi-disciplinary approach combines techniques from computer vision, natural language processing, and signal processing to overcome the challenges posed by the heterogeneity and dynamics of emotions.

The MAN model incorporates both peripheral and central networks to inject cross-modal attention. This allows the model to consider the influence of external factors like audience reactions and the context of ongoing conversations in group settings. By integrating discriminative and complementary mode-specific data patterns, the model can generate instance-specific multimodal descriptors, condensing spatial and temporal features into speaker-level and utterance-level representations.

The impacts of this research are significant not only in terms of classification performance improvement but also in enhancing user understanding of emotion predictions. The proposed AMuSE model includes a Multimodal Explainability Visualization module, which provides explanations for each emotion prediction. This brings transparency to the decision-making process of the model, enabling users to comprehend the reasoning behind the emotions detected.

These concepts are closely related to the wider field of multimedia information systems and have implications for various applications such as virtual reality, augmented reality, and artificial reality. These technologies can benefit from improved emotion recognition techniques to create more immersive and engaging experiences. By understanding users’ emotions, these systems can adapt and respond accordingly, enhancing user satisfaction.

In conclusion, the proposed Multimodal Attention Network and AMuSE model contribute to the development of intelligent agents capable of understanding and responding to human emotions during group conversations. The multi-disciplinary nature of this research, combining knowledge from various domains, is crucial in tackling the challenges posed by heterogeneous and dynamic emotion recognition. This article demonstrates the potential impact of these concepts on the wider field of multimedia information systems and related technologies like animations, artificial reality, augmented reality, and virtual realities.
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