###### Abstract  
Speech audiometry is a vital tool in assessing an individual's ability to perceive and comprehend speech, traditionally requiring specialized testing that can be time-consuming and resource -intensive. This paper approaches a novel use of deep learning to predict speech audiometry using pure-tone audiometry (PTA) data. By utilizing PTA data, which measure hearing sensitivity at specific frequencies, we aim to develop a model that can bypass the need for direct speech testing. This study investigates two neural network architectures: a multi-layer perceptron (MLP) and a one-dimensional convolutional neural network (1D-CNN). These models are trained to predict key speech audiometry outcomes, including speech recognition thresholds and speech discrimination scores. To evaluate the effectiveness of these models, we employed two key performance metrics: the coefficient of determination (R\({}^{2}\)) and mean absolute error (MAE). The MLP model demonstrated predictive solid power with an R\({}^{2}\) score of 88.79% and an average MAE of 7.26, while the 1D-CNN model achieved a slightly higher level of accuracy with an MAE score of 88.35% and an MAE of 6.90. The superior performance of the 1D-CNN model suggests that it captures relevant features from PTA data more effectively than the MLP. These results show that both models hold promise for predicting speech audiometry, potentially simplifying the audiological evaluation process. This approach is applied in clinical settings for hearing loss assessment, the selection of hearing aids, and the development of personalized auditory rehabilitation programs. speech audiometry; pure-tone audiometry; multi-layer perceptron; 1D-convolutional neural network; regression analysis in audiology; deep learning for hearing assessment; R\({}^{2}\) score; mean absolute error +
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Speech audiometry is used to more accurately diagnose speech-hearing levels through various methods, including evaluating patients' ability to recognize words and sentences or assessing their listening capacity in noisy environments [4]. However, speech audiometry has its drawbacks. Since the test uses devices such as headphones, it inherently relies on the patient's subjective evaluation. This reliance may reduce the accuracy of the speech audiogram, potentially leading to diagnostic or measurement errors by doctors or patients, and various factors such as environmental conditions and human variables may affect the reliability of the speech-hearing results. Pure-tone audiometry (PTA) is a standard testing method to evaluate auditory function. Pure-tone audiometry data are primarily measured using testing equipment, which records the patient's hearing data. The test involves playing pure tones at specific frequencies to the patient, and the patient's ability to detect the minimum sound intensity is measured as the threshold value, which is used to determine the type and degree of hearing loss [5]. Pure-tone audiometry consists of air conduction (AC) and bone conduction (BC) tests. AC measures hearing through the outer, middle, and inner ear, while BC measures bone conduction hearing. Each method measures both ears' hearing sensitivity or threshold value by adjusting the intensity of stimuli at different frequencies [6]. Pure tones are measured starting at 125 Hz, the lowest frequency detectable by humans, and continue at 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, and 4000 Hz. Therefore, PTA measures an individual's hearing status at specific frequencies by determining each frequency's decibel (dB) value. These data are quantitatively evaluated to diagnose various hearing impairments, including hearing loss, hearing disabilities, hearing aid selection, treatment cycle determination, auditory rehabilitation, and treatment efficacy assessment [7].