## 4 Results
### Experiment Setup  
Using the automatic construction system of the Zhuang database, a 200 h database of Zhuang speech with annotations was built for the training of speech recognition models by filtering the videos of Guangxi TV over the past ten years. The major dialect of this database is Wuming Zhuang, and the conversation topics include society, finance, science and technology, entertainment, and health, all of which are used for the training of ASR. SpecAugment [48; 49] was employed to augment data and prevent overfitting during training. Specific parameters were set, including a frequency mask size parameter (\(F=10\)) and the time and frequency mask (\(mT=mF=2\)). The input features are 80-dimensional  
Figure 10: Physical drawing of mini terminal platform structure.  
mel-scale log filter bank spectrograms, which were calculated with a 25 ms window and a 10 ms shift. The publicly available dataset AISHELL-1 [50] was also used as a pre-feasibility validation of ASR. The 178 h open-source AISHELL-1 speech database used high-fidelity microphones to record 400 speakers from diverse accent regions in China, involved in intelligent households, drones, industrial production, and other fields. The parallel corpus for MT was selected from the Asian Scientific Papers Excerpt Corpus (ASPEC) version 1.0 [51], with approximately 680,000 parallel utterances. The corpus was divided into 610,000 pairs of training data, 2000 pairs of development data, and 2000 pairs of test data. The development and test data were randomly selected. In addition, 5000 language pairs were randomly selected from the publicly available bilingual dataset OPUS JA-ZH for manual alignment, which was used to validate the generalization ability of the model. The model was trained on a single Nvidia RTX 3090 GPU using the open-source deep learning framework PyTorch, version 2.0.1. To optimize training efficiency and maximize GPU memory usage, the data loader was set up to sort and pack statements based on frame length, and then randomly select these statements to feed to the model.  
For the speech recognition model, the corresponding SAformerNet-S and M were designed using Conformer-S and M under the reference of the architectural parameters, keeping the model size constant, and the hyperparameters of the model were set as shown in Table 1. The utilization of four attention heads and setting the hyperparameter \(g\) to 3 provides a good balance between computational effort and accuracy. CER evaluates the error rate between the predicted text and the original text, a lower CER indicates better ASR performance. The machine translation model's encoder network comprises six modules that are identical in structure. Each module consists of two sublayer structures, including Multi-head Self-attention and a Fully connected Feed-forward Network (FFN), which perform residual concatenation and normalization on the output of each sublayer. The decoder also has six modules that are identical in structure, with one more Multi-head Attention Network layer than the encoder module. The vector size of the word is 512 and the output dimension of the first linear layer is 2048 using the 8-head attention mechanism. The Byte Pair Encoding (BPE) segmentation algorithm was utilized. Word list size was set to 32,000, and the batch size was set to 32 due to hardware constraints. The effectiveness of machine translation models is conducted with the BLEU. The degree of similarity between two sentences can be determined by the BLEU, which calculates a composite score. A higher score indicates improved machine translation quality. The training data, validation data, and verification data used in the experiment were randomly selected and do not overlap with each other.