AdaBoost based Random forest model for Emotion classification of Facial images

Gubbala, Kumari; Kumar, M. Naveen; Sowjanya, A. Mary

doi:10.1016/j.mex.2023.102422

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Before the field of deep learning, people mostly relied on traditional machine learning algorithms, such as the Random Forest model [28], support vector machine model [4], Gaussian mixture model [29], Hidden Markov model [3], etc., to identify emotions. However, the early model is not yet perfect and has great limitations in capturing the relationship of emotional data.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Efficient Graph-Based Hybrid Learning for Speech Emotion Recognition on Humanoid Robot

Wu,

Xu,

Seng

et al. 2024

Electronics

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This paper presents a novel deep graph-based learning technique for speech emotion recognition which has been specifically tailored for energy efficient deployment within humanoid robots. Our methodology represents a fusion of scalable graph representations, rooted in the foundational principles of graph signal processing theories. By delving into the utilization of cycle or line graphs as fundamental constituents shaping a robust Graph Convolution Network (GCN)-based architecture, we propose an approach which allows the capture of relationships between speech signals to decode intricate emotional patterns and responses. Our methodology is validated and benchmarked against established databases such as IEMOCAP and MSP-IMPROV. Our model outperforms standard GCNs and prevalent deep graph architectures, demonstrating performance levels that align with state-of-the-art methodologies. Notably, our model achieves this feat while significantly reducing the number of learnable parameters, thereby increasing computational efficiency and bolstering its suitability for resource-constrained environments. This proposed energy-efficient graph-based hybrid learning methodology is applied towards multimodal emotion recognition within humanoid robots. Its capacity to deliver competitive performance while streamlining computational complexity and energy efficiency represents a novel approach in evolving emotion recognition systems, catering to diverse real-world applications where precision in emotion recognition within humanoid robots stands as a pivotal requisite.

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