## 5 Experimental Results and Discussion  
In this section, we describe the data used in the experiment and the findings of the experiment on the performance of the baselines, including the proposed model. To ensure consistency and reliability of the analysis, LSA examines only a set number of utterances, such as 3-50 among the utterances generated by children [37, 38, 39, 40]. The data we collected were data that speech-language pathologists judged to be normal, and the sample size was very small. To increase the sample size and define the evaluate criteria, we divide the number of each child's utterances by 20. The description of the generated data is shown in Table 5.  
We compared the performance of various deep learning-based approaches and large-scale language models in the experiment, which are described as follows:  
* **LSTM-M2O**. A model with a many-to-one architecture for Long Short-Term Memory (LSTM);
* **LSTM-M2M**. A model with a many-to-many architecture for LSTM;
* **CNN-MP**. A model with max-pooling operation for CNN;
* **CNN-AP**. A model with average-pooling operation for CNN;
* **CNN-MP/AP**. A model with max-pooling and average-pooling operation simultaneously for CNN [41];
* **CNN + LSTM**. A model that training the output of a CNN-MP on an LSTM-M2M [42];
* **LSTM + CNN**. A model that training the output of a LSTM-M2M on an CNN-MP;
* **Transformers**. A model with basic Transformers architecture for classification task. We also compared the models that were trained with only word and the models that were trained with both word and part-of-speech. Finally, in the proposed method, we compared the usage of the Siamese network to the non-use of the Siamese network to observe if our hypothesis was true. The evaluation metric was validated using 5-fold cross validation as an average. Table 6 shows the outcomes of the experiments. As shown in Table 6, regardless of the large-scale language model, LSTM is not suitable to classify children's language development levels. As a consequence, we consider it is difficult to discover linguistic rules in children's utterances as time-series characteristics. In contrast, CNN has confirmed that relevant features for screening children's language development levels in word and part-of-speech appearing in a certain window size are found. With an accuracy of 78%, our proposed model outperformed the other models. It is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Age** & **Data** & **Length (Avg.)** \\ \hline
2-year-old & 113 & 41.17 \\
4-year-old & 303 & 67.55 \\
6-year-old & 283 & 79.30 \\ Total & 699 & 62.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Description of data used in experiment. considered that by sharing weights for word and part-of-speech, linguistic rules could be used effectively. However, the CNN-MP/AP model using only word also showed a fairly high performance of 77.2%. The confusion matrix was used to compare the performance of the two models, and the findings are shown in Table 7.  
As shown in Table 7, the two models correctly classified 2-year-old children, but that there was a difference in performance between 4-year-old and 6-year-old children. This issue emerged in the majority of models, and even the low-performance models could not correctly classify the 2-year-old. In fact, children's language development rapidly develops