A Lightweight Hybrid Architecture for Speech Recognition

This study proposes a lightweight hybrid architecture for speech recognition, integrating four convolutional layers with spectral normalization, two adaptive max-pooling layers, and two fully connected layers with dropout regularization. The design emphasizes computational efficiency through kernel pruning while maintaining consistent inference performance across hardware platforms. Evaluation using noisy speech datasets demonstrates robust recognition accuracy and real-time processing capabilities. Deployment validation confirms operational stability in edge computing environments, confirming suitability for resource-constrained applications requiring energy-efficient speech recognition.