## 6 Conclusions  
In this study, deep learning was introduced to objectively and readily identify a child's language development level, and the feasibility of a large-scale language model was investigated. In this case, we employed linguistic rules such as word and part-of-speech and the CNN based on the notion of Siamese networks for effectively using them. Our proposed model showed an accuracy of about 78%, and it was verified that a large-scale language model was inadequate for solving this challenge.  
The deep learning-based approach we propose has several advantages. First, because the challenge is solved using an end-to-end architecture, there is no need for a separate language analysis. In LSA, utterances had to be transcribed according to established rules in process of using LSA software, and a separate language analysis tool was required. As a reason, the performance of language analysis tools has a significant impact on LSA, but we have avoided this challenge. Second, the deep learning-based approach is capable of generalized inference (or evaluation). In contrast, LSA evaluates the outcomes of the analysis by comparing them to those of the same age group. Furthermore, in order to improve the reliability of evaluation, it is important to acquire transcription data on a regular basis, which is difficult to perform. Finally, deep learning-based approach can discriminate between children who have delayed language development and those who have rapid language development.  
However, we also discovered some limitations: First, collecting high-quality data is challenging due to a lack of interest in children's language development. Second, there is a lot of inappropriate data that cannot be used within the collected data. Third, the quality of the data collected may vary based on external factors such as the expert's competency or the child's personality.  
In the future, we will find a linguistic pattern that can effectively discriminate between 4-year-old and 6-year-old. We will also create a model architecture that can effectively train these features. In addition, we will analyze the transcription data to see if there are any outliers and, if there are, their causes. Furthermore, we will explore methods to utilize the large-scale language model in this challenge. As a consequence, we anticipate the ability to construct a deep learning-based model with an end-to-end architecture that can more correctly evaluate children's language development levels than previously possible.  
Conceptualization, Y.-S.K.; data curation, Y.-K.L. and B.-D.O.; formal analysis, B.-D.O., [PERSON]-D.K. and C.-Y.P.; funding acquisition, Y.-K.L. and Y.-S.K.; methodology, B.-D.O.; project administration, Y.-S.K.; resources, Y.-K.L., C.-Y.P. and [PERSON]-D.K.; supervision, Y.-S.K.; validation, B.-D.O.; writing--original draft, B.-D.O.; writing--review and editing, Y.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF) (NRF-201951A5A2A03052093), Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (NO. 2021-0-02068, Artificial Intelligence Innovation Hub (Seoul National University)) and 'R&D Program for Forest Science Technology (Project No. 2021390A00-2123-0105)' funded by Korea Forest Service (KFPI).  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and is approved by the Institutional Review Board of Hallym University (HIRB-2019-036).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.