# Multimodal Seed Data Augmentation for Low-Resource Audio Latin Cuengh Language
## 4 Experimental Results and Analyses
### Text Classification
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Layer 1** & \multicolumn{3}{c}{**Layer 2**} & **Layer 3** & \multicolumn{3}{c}{**Twenty-Class**} & \multicolumn{3}{c}{**Binary-Class**} \\ \hline
**L** & **A** & **C** & **LA** & **CA** & **LAC** & **ACC (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **ACC (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** \\ \hline \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 67.63 & 66.02 & 66.38 & 65.67 & 86.55 & 86.23 & 86.87 \\ \hline \(\times\) & \(\times\) & \(\  
layers, the combination of layers 1 and 2 surpasses that of layers 2 and 3. We surmise that layer 1's comprehensive feature extraction, coupled with the effective text and speech fusion in layer 2, contributes significantly to the model's performance, with layer 3's MLP operation serving as an enhancement rather than a foundational component. Lastly, to evaluate the model's performance under low-resource conditions, we conducted comparative experiments using training datasets of varying sizes ([1%, 5%, 10%, 20%, 40%]) across multi-classification and binary classification tasks. In this process, the dataset is randomly selected according to the aforementioned ratios to simulate the data augmentation effect of the model under low-resource conditions. For instance, in our experiments, we used as little as 1% of the data volume. In this extreme scenario, the model's performance was highly unstable. However, we found that, in the binary classification task, our method achieved a relatively satisfactory performance even with extremely scarce sample data. The results of these experiments are depicted in Figures 5 and 6. Figure 5 illustrates the impact of training data proportion on the model's precision in the multi-text classification task. When the training data constitute less than 10% of the total dataset, the model's precision noticeably decreases. However, once the training data reaches 40% of the total, the model's precision surpasses 90% of the result achieved with the complete dataset, consistently outperforming other models across various data proportions. Figure 6 compares various models' performances in the binary classification task. Even with training data comprising as little as 1% of the total, our model's precision exceeds 40%. At a 20% training data threshold, our model achieves 80% precision on both metrics. While the increase in training data leads to a plateau in the growth rate of precision and F1 score, our model still surpasses others under low-resource conditions. This superior performance is attributed to the model's structural design, which is tailored for low-resource data training, and the implementation of a seed data augmentation method that adeptly addresses feature scarcity. Our model distinguishes itself from other contemporary multimodal models by its lightweight nature. It selects seed data for enhancement under the text modality, thereby  
Figure 5: Low-resource performance on twenty-class setting. Figure 6: Low-resource performance on binary-class setting. reducing the time required for pre-training and fine-tuning in the multimodal fusion process. To substantiate our model's efficiency, we conducted experiments comparing the energy efficiency ratio with several benchmark models; the results are depicted in Figure 7.  
Figure 7 provides a comparative analysis of training, fine-tuning, and inference times across different models. For models utilizing a single text modality, the training time is relatively brief. However, the inclusion of both text and audio modalities substantially increases the training duration. Our model exhibits the lowest time consumption across all three metrics, affirming its high efficiency. The figure utilizes rectangles to represent the amount of time consumed, where a lower rectangle indicates less time and higher efficiency. The training time is measured in hours, fine-tuning in minutes, and inference in seconds.