Detecting Hearing Impairment Through Localizing Abnormal Speech Patterns

Detecting early signs of hearing impairment may help prevent age-related cognitive decline and associated disorders. However, conventional methods like pure tone audiometry are often subjective and insufficient for early detection. In this paper, we propose a novel, subject-agnostic approach for automatically detecting hearing impairment by localizing abnormal speech patterns during word recognition tests. To achieve this, we collected an audio dataset stimulated by the standard word list that is commonly employed in clinical assessments of hearing loss. This dataset is segmented into phoneme-level audio, where we then extract and model the acoustic features exclusively from normal speech patterns in a semi-supervised setting. For inference, we compute anomaly scores at the phoneme level by comparing them with these learned normal patterns, enabling precise localization of abnormalities within speech. Our method demonstrates a high accuracy rate of 70.73% in detecting six levels of hearing impairment severity, showcasing its potential for assisting in early assessment.