## 4 Experiments  
### Experimental Setup  
To validate the feasibility of the proposed model fusion and improved decoding algorithms, we implemented three different network architectures: Conformer, Unified_Conformer, and U2++_Conformer. The detailed experimental configurations for these models are presented in Table 1.  
Table 1 summarizes the key hyperparameter configurations used in the experiments, including the structure of the encoder and decoder, optimizer settings, and dataset parameters. Taking Conformer as an example, its encoder consists of 12 stacked layers, each with an output dimension of 256, utilizing the Swish activation function. The decoder comprises 6 stacked layers, with a CTC weight of 0.3, label smoothing weight of 0.1, and a batch size of 12. These parameters were derived from existing studies [46] and fine-tuned to align with specific task requirements. Building on this foundation, the Unified_Conformer and U2++_Conformer models introduce modifications to the model architecture and data processing strategies. Unified_Conformer employs a reduced learning rate (0.001) to enhance training stability. In contrast, U2++_Conformer reduces the number of decoder layers from 6 to 3 and incorporates a reverse sequence modeling weight (Reverse Weight = 0.3) to improve the capture of global features.  
Additionally, during model training, the Adam optimizer was employed to dynamically adjust critical hyperparameters, thereby enhancing training efficiency and convergence [47].  
Table 2 illustrates the unified network architecture of the three models, comprising an input layer, convolutional layers, positional encoding, Conformer blocks, and a fully connected layer. The input, represented as an 80-dimensional feature sequence, undergoes initial processing through 2D convolution to generate a 256-dimensional hidden state. This hidden state is then passed to the encoder, which is composed of 12 Conformer blocks. Each Conformer block includes feed-forward layers, self-attention mechanisms, and convolutional modules. Finally, the fully connected layer maps the encoded fea  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Composition** & **Conformer** & **Unified\_Conformer** & **U2++\_Conformer** \\ \hline \multirow{3}{*}{Encoder Params} & Output Dim: 256 & Output Dim: 256 & Output Dim: 256 \\  & Number of Blocks: 12 & Number of Blocks: 12 & Number of Blocks: 12 \\  & Activation: Swish & Activation: Swish & Activation: Swish \\ \hline \multirow{3}{*}{Decoder Params} & Number of Blocks: 6 & Number of Blocks: 6 & Number of Blocks: 3 \\ \cline{2-4}  & CTC Weight: 0.3 & CTC Weight: 0.3 & CTC Weight: 0.3 \\ \cline{1-1}  & LSM Weight: 0.1 & LSM Weight: 0.1 & LSM Weight: 0.1 \\ \cline{1-1}  & Reverse Weight: None & Reverse Weight: None & Reverse Weight: 0.3 \\ \hline \multirow{3}{*}{Dataset Params} & Max Length: 2000 & Max Length: 2000 & Max Length: 40960 \\ \cline{1-1}  & Min Length: 50 & Min Length: 50 & Min Length: 0 \\ \cline{1-1}  & Batch Size: 12 & Batch Size: 12 & Batch Size: 12 \\ \hline Max Training Epochs & 100 & 100 & 100 \\ \hline Optimizer Params & Learning Rate: 0.004 & Learning Rate: 0.001 & Learning Rate: 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameter configurations.  
tures to a 5002-dimensional output space. The 5002-dimensional output corresponds to the size of the model's vocabulary, representing the total number of possible word or character categories.  
The hardware environment for the experiments includes an NVIDIA RTX 4070 GPU with 16.0 GB of RAM, running on a 64-bit Ubuntu 18.04 operating system. The software environment is Python 3.8.