Enhanced semantic communication schemes for speech signals

Two new models for semantic communication systems are proposed. The first model incorporates the convolutional block attention module, which considers attention techniques in both the channel and spatial domains. The second model applies the efficient channel attention (ECA) network with reduced complexity. Experimental results demonstrate that the convolutional block attention module‐equipped model improved signal‐to‐distortion ratio performance by 25%$25\%$ at a signal‐to‐noise ratio of 0dB$0 \text{ dB}$ while maintaining a similar number of parameters compared to the existing model using squeeze‐and‐excitation network. Meanwhile, the efficient channel attention‐equipped model reduced parameters by approximately 48%$48\%$ without any degradation in performance compared to the existing model. Two models to improve semantic communication systems are introduced. The first utilizes CBAM, considering attention in both channel and spatial domains, while the second uses the ECA network for complexity reduction. Experimental results demonstrate that the CBAM‐enhanced model notably enhances SDR performance in comparison to the existing SENet model, while the ECA‐equipped model achieves significant parameter reduction without sacrificing performance.