## 3 Results
### Implementation Details  
The proposed semantic-aware transmission system is implemented using TensorFlow 2.10.0 and trained on an NVIDIA RTX 2080 Ti GPU. The training configuration is as follows:  
* Batch size: 64
* Loss/cost function: MSE
* Optimization: RMSProp
* Learning rate: 0.0005
* Training epochs: 750
* Fading model: Rayleigh
* Training SNR: 8 dB  
The semantic encoder consists of following components as shown in Table 1:We use SE-ResNet blocks as they are commonly implemented in semantic speech encoding in the literature [26]. Thanks to their attention-based and excitation mechanism, SE blocks adaptively facilitate feature extraction with essential speech information. The selection of hyperparameters, including the training SNR of 8 dB and the number of filters, was based on empirical tuning in place of an exhaustive grid search. This work mainly aims to demonstrate a proof of concept, and these hyperparameters were chosen as reasonable settings rather than globally optimized values. Future work could further refine these choices using hyperparameter search methods. In the semantic compression module, the first 2D CNN layer employs a ReLU activation function, while the second layer does not incorporate any activation function. At the receiver, a symmetric architecture is employed for semantic decoding, where the semantic expander reconstructs feature dimensions before the semantic interpreter synthesizes the speech waveform. The first 2D transposed CNN layer in the semantic expander operates without an activation function, whereas the second layer applies the ReLU activation function. Through our empirical analyses, this configuration resulted in the lowest reconstruction error. The last layer of the semantic compressor is designed without an activation function to ensure semantic information remains within an appropriate range [11; 27]. Notably, we determined that omitting activation functions in specific layers led to the best performance in our case. In particular, the second layer of the semantic compressor and the first layer of the semantic expander achieved the best results without activation functions. Additionally, the output of the semantic interpreter showed improved performance when no activation function was applied. The training phase of the proposed system differs from conventional communication models as it does not involve explicit channel encoding, modulation, or decoding operations. These traditional signal processing components are inherently non-differentiable, which makes them unsuitable for the backpropagation-based optimization commonly used in deep learning models [13]. The conventional transmission system follows a standard digital communication approach, as summarized in Table 2.  
We compare our proposed digital semantic-aware system against the conventional approach as shown in Table 2. It is important to note that the conventional system under discussion is maintained in its current form within the proposed new system. \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Component** & **Subcomponent** & **Layers** & **Filters** & **Activation** \\ \hline \multirow{3}{*}{Semantic Encoder} & \multirow{3}{*}{Semantic Extractor} & 2 × 2D CNN & 64 & ReLU \\  & & 5 × SE-ResNet & 64 & ReLU \\ \cline{2-5}  & Semantic Compressor & 2 × 2D CNN & 128 & ReLU/None \\ \hline \multirow{3}{*}{Semantic Decoder} & Semantic Expander & 2 × 2D Transposed CNN & 128 & None/ReLU \\ \cline{2-5}  & \multirow{3}{*}{Semantic Interpreter} & 5 × SE-ResNet & 128 & ReLU \\ \cline{1-1}  & & 2 × 2D Transposed CNN & 128 & ReLU \\ \cline{1-1}  & & 1 × 2D CNN & 1 & None \\ \hline \hline \end{tabular} Note: Slashes (/) denote layer-specific activation functions.  
\end{table} 
Table 1: Proposed semantic-aware digital speech communication parameter settings.