## 1 Introduction  
In recent years, deep learning and machine learning have driven groundbreaking advancements across various fields. In particular, these technologies have demonstrated exceptional performance in learning and predicting complex data patterns in the medical domain, attracting significant attention from researchers and healthcare professionals. Medical data inherently contain vast amounts of intricate information, with various factors related to a patient's health status often exhibiting interdependencies [1]. Deep learning and machine learning are powerful tools for efficiently processing such data and extracting meaningful insights, especially when modeling nonlinear and complex relationships [2]. Utilizing these technological advantages, deep learning has been actively applied in various medical applications, including medical image analysis, pathological data classification, and biometric signal processing [3]. Machine learning excels in analyzing structured data and making predictions, playing an equally vital role in medical data analysis [4]. Against this backdrop, the present study aimed to develop a deep learning- and machine learning-based multi-class classification model to predict speech audiometry results using pure-tone audiometry (PTA) data, a crucial diagnostic tool for evaluating auditory health. PTA assesses hearing thresholds by measuring a patient's minimum sound intensity at specific frequencies, thereby evaluating hearing across different frequency ranges [5]. In contrast, speech audiometry evaluates a patient's ability to recognize and comprehend speech in everyday conversations, which is closely linked to real-world language perception abilities [6]. Although both tests serve complementary roles in diagnosing and managing hearing impairments, speech audiometry is often time-consuming and costly, making it impractical to administer to all patients uniformly [7]. Speech audiometry relies on headphones and other equipment for measurement, making it inherently dependent on the subjective assessment of both the patient and the audiologist. This dependency can lead to reduced accuracy in speech audiometry results, increasing the risk of diagnostic and measurement errors by clinicians or patients. Furthermore, various external factors, such as environmental noise, individual differences, dialects, and other human-related factors, may contribute to lower reliability in speech audiometry assessments. The primary objective of this study was to develop and compare models that predict speech audiometry outcomes solely from PTA data, identifying the most effective model for accurate predictions. This approach could potentially reduce the need for additional testing, thereby enhancing diagnostic efficiency and the effectiveness of treatment. If speech audiometry results can be accurately predicted from pure-tone audiometry data, it would save medical resources and patient time and enable a more rapid and precise approach to diagnosing and managing hearing impairments [8]. Machine learning and deep learning algorithms are pivotal in developing such predictive models. While machine learning can classify or predict speech recognition ability based on learned relationships within auditory data, deep learning excels at handling large-scale data and capturing complex patterns to model nonlinear relationships. Specifically, as a fundamental hearing assessment, PTA consists of air conduction (AC) and bone conduction (BC) tests. Air conduction audiometry evaluates sound transmission through the external ear and middle ear, whereas bone conduction audiometry measures sound transmission through the skull [9]. Deep learning models can use these data to learn intricate patterns, improving predictive accuracy. This study aimed to explore new possibilities in medical data analysis by integrating deep learning and machine learning, analyzing the effectiveness of different models in predicting speech audiometry outcomes from pure-tone audiometry data.