Development and Comparison of Machine Learning and Deep Learning Models for Speech Audiometry Prediction

Jae sung Shin, Jun Ma, Mao Makara, Nak-Jun Sung, Seong Jun Choi, Sung yeup Kim, Min Hong

Hearing loss significantly impacts daily communication, making accurate speech audiometry (SA) assessment essential for diagnosis and treatment. However, SA testing is time-consuming and resource-intensive, limiting its accessibility in clinical practice. This study aimed to develop a multi-class classification model that predicts SA results using pure-tone audiometry (PTA) data, enabling a more efficient and automated assessment. To achieve this, we implemented and compared MLP, RNN, gradient boosting, and XGBoost models, evaluating their performance using accuracy, F1 score, log loss, and confusion matrix analysis. Experimental results showed that gradient boosting achieved the highest accuracy, 86.22%, while XGBoost demonstrated a more balanced classification performance. The MLP achieved 85.77% and the RNN achieved 85.41%, exhibiting relatively low accuracy, with the RNN showing limitations due to the low temporal dependency of PTA data. Additionally, all models faced challenges predicting class 2 (borderline hearing levels) due to overlapping data distributions. These findings suggest that machine learning models, particularly gradient boosting and XGBoost, outperform deep learning models in SA prediction. Future research should focus on feature engineering, hyperparameter optimization, and ensemble approaches to enhance performance and validate real-world applicability. The proposed model could contribute to automating SA prediction and improving hearing assessment efficiency and patient care.