USE-Net: SE-Block Enhanced U-Net Architecture for Robust Speaker Identification

Conventional speaker identification systems often fall short in capturing the diverse variations present in speech data due to fixed-scale architectures. In this research, we propose a CNN based architecture, USENet, designed to overcome these limitations. Leveraging two key techniques, our approach achieves superior performance on the VoxCeleb 1 Dataset without any pre-training. Firstly, we adopt a U-net inspired design to extract features at multiple scales, empowering our model to capture speech characteristics effectively. Secondly, we introduce the squeeze and excitation block to enhance spatial feature learning. The proposed architecture showcases significant advancements in speaker identification, outperforming existing methods, and holds promise for future research in this domain.