arXiv:2606.03359v1 Announce Type: cross Abstract: Speech emotion recognition is an important component of modern human-computer interaction systems. However, many state-of-the-art approaches rely on large pretrained models with high computational and memory requirements, limiting their applicability. This paper proposes ResLSTM-SA, a lightweight architecture that integrates residual connections with soft attention within an LSTM-based framework. Evaluated on the RAVDESS dataset under strict speaker-independent partitioning, the proposed model outperforms conventional attention-based LSTM basel
The continuous drive for more efficient and less resource-intensive AI models for real-world applications in human-computer interaction pushes research toward optimized architectures like ResLSTM-SA.
This development allows for more accessible and widely deployable speech emotion recognition in resource-constrained environments, broadening the practical applications of AI in interactive systems.
The ability to perform speech emotion recognition with lightweight models means advanced AI capabilities can be integrated into a broader range of devices and platforms, reducing reliance on powerful, centralized compute.
- · Edge AI developers
- · Human-computer interaction system providers
- · Consumer electronics manufacturers
- · Companies relying solely on large, computationally expensive AI models
- · Data center providers (marginally, for this specific use case)
More widespread deployment of embedded speech emotion recognition in everyday devices and applications.
Improved user experience and personalization in devices due to better understanding of emotional states without constant cloud connectivity.
Enhanced accessibility of mental health monitoring tools or assistive technologies that rely on emotional cues, fostering deeper human-AI symbiosis on a personal level.
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Read at arXiv cs.CL