Replication Data for: Modeling the impact of cochlear nerve degeneration on speech recognition performance

Cochlear nerve degeneration (CND), the loss of synapses between inner hair cells and auditory nerve fibers (ANFs), has emerged as a leading candidate for the neural basis of \"hidden hearing loss\", a condition in which listeners experience speech-in-noise difficulties that cannot be fully explained by audiometric thresholds. This form of primary neural de-afferentation preferentially affects low- and medium-spontaneous rate (SR) fibers, which are critical for encoding acoustic features such as amplitude modulations, especially under challenging listening conditions such as noisy backgrounds. Although CND is well established in animal models and post-mortem human studies, its perceptual consequences remain poorly understood due to the inability to directly assess synaptic integrity in living humans. Here, we combine behavioral testing, physiologically grounded modeling of ANFs, and deep neural network (DNN) decoding to evaluate how SR-specific fiber loss degrades speech recognition in noise. Audiometric thresholds and word recognition scores for time-compressed, reverberant NU-6 words were obtained from 395 cognitively normal adults aged 18–80. To isolate the neural contribution to speech encoding, we simulated ANF activity using a phenomenological model of the auditory periphery under three CND profiles, varying the survival of SR fiber classes. Neurograms were input to two DNN architectures trained on speech classification tasks. While both networks achieved high accuracy, only the deeper, more constrained model produced recognition scores consistent with human performance and showed sensitivity to CND. These findings provide a mechanistic link between synaptopathy and speech-in-noise difficulties and establish a computational framework for evaluating the perceptual impact of hidden hearing loss