Abstract Speech enhancement plays a crucial role in improving speech clarity and intelligibility in noisy environments, benefiting applications such as telecommunications, assistive hearing, voice-activated systems, and automatic speech recognition. Although transformer and conformer-based models achieve state-of-the-art performance, their complexity makes real-time deployment on resource-constrained devices impractical. In contrast, convolutional neural networks offer efficient on-device deployment due to their lower...
