Personalized prediction of speech intelligibility for hearing-impaired listeners using a physiological model of the human ear

Abstract

Due to the problems of occlusion effect, insufficient high-frequency gain, and acoustic feedback in conventional hearing aids, middle ear implants as a new type of hearing device have become an important compensatory tool for the treatment of patients with moderate to severe hearing loss. However, current speech intelligibility (SI) models are inadequate for predicting SI in hearing-impaired (HI) listeners after middle ear implants implantation. Moreover, the compensatory performance of middle ear implants before implantation remains unknown due to the invasive nature of the surgical procedure. Therefore, this study proposes a novel SI model that can predict the compensatory effects on SI after middle ear implants implantation. The model combines a physiologically nonlinear auditory preprocessing front-end with a short-term correlation analysis back-end. In normal-hearing (NH) listeners, the model accurately predicts speech reception thresholds (SRTs) and masking release under steady-state and fluctuating noise conditions. For HI listeners, the model modifies the parameters of outer hair cells and inner hair cells in the preprocessing front-end to simulate the patient’s audiogram, achieving excellent predictive capability for HI listeners’ test data. Overall, the proposed SI model can be used for the optimal design and algorithm fitting of middle ear implants tailored to patients with varying degrees of hearing loss, offering valuable insights for the clinical treatment of HI listeners.