DeepSC‐S‐MECA: A Hybrid Mamba‐ECA Framework for Robust Semantic Speech Communication

ABSTRACT This paper proposes DeepSC‐S‐MECA, a semantic communication model for improving speech semantic reconstruction. The model combines a Mamba Transformer (MambaTrans) block, which integrates a State Space Model (SSM) into a Transformer to capture temporal and contextual dependencies in sequential signals, and an ECA‐ResNet block, which applies efficient channel attention (ECA) to emphasize informative features. Experimental results under AWGN and Rician channels show that DeepSC‐S‐MECA achieves higher SDR and PESQ than DeepSC‐S‐CBAMR and DeepSC‐S‐ECAR, providing robust speech reconstruction while reducing parameters and FLOPs by about 39% compared with DeepSC‐S‐CBAMR.