## 5 Conclusions  
In this study, we proposed a method to predict speech audiometry scores using PTA data through MLP and 1D-CNN models, comparing the performance of models. The  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Software/Parameters** & **Value** \\ \hline Windows 10 & 64-bit \\ Programming language & Python, Keras, TensorFlow \\ CPU & AMD Ryzen 7 5700G \\ GPU & NVIDIA GeForce RTX 4070 Ti \\ RAM & 64 GB \\ Batch size & 16 \\ Validation split & 0.2 \\ Test split & 0.2 \\ Optimizer & Adam \\ Learning rate & 0.001 \\ Loss function & Huber \\ Epochs & 300 \\ Dataset & 21,893 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Software and simulation parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **R\({}^{2}\) Score** & **MAE** \\ \hline MLP & 88.79 & 7.26 \\
1D-CNN & 88.35 & 6.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: R\({}^{2}\) scores and MAEs of MLP and 1D-CNN models.  
results showed that the 1D-CNN achieved 4.96% better MAE performance than the MLP, with both models recording high prediction accuracy, achieving R\({}^{2}\) scores of 88.29% and 88.35%, respectively.  
These findings suggest that both models effectively captured the non-linear patterns in auditory data, with the 1D-CNN particularly excelling in learning critical frequency bands in sequential data, reinforcing its suitability for predicting speech audiometry scores. Using these models could allow for more accurate identification of the degree and nature of speech audiometry loss, offering valuable insights for communication impairment assessment, hearing aid selection and adaptation, hearing loss treatment planning, and the design of auditory rehabilitation programs. This study highlights the potential of deep learning to improve the accuracy and efficiency of predicting speech audiometry outcomes.  
For future work, we plan to explore a broader range of deep learning models and utilize larger datasets to further enhance predictive performance. Additionally, it is crucial to evaluate the applicability of these models in clinical settings and investigate the practical benefits to patients. Expanding on these efforts could significantly advance the role of deep learning in auditory assessment and rehabilitation.  
[PERSON] contributed to conceptualization, methodology, validation, writing--review and editing, supervision, funding acquisition, and project administration. [PERSON] contributed to conceptualization, formal analysis, investigation, resources, software, data curation, writing--original draft preparation, and visualization. [PERSON] and [PERSON] contributed to formal analysis, validation, data curation, and writing--review and editing. [PERSON] contributed to conceptualization, methodology, validation, resources, data curation, writing--review and editing, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the BK21 FOUR (Fostering Outstanding Universities for Research) Grant, No. 5199990914048, and was supported by the Soonchunhyang University Research Fund.  
The data used in this research were reviewed and approved by the Institutional Review Board (IRB No.: 2021-06-040) of Soon Chun Hyang University Cheonan Hospital.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflicts of interest.