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Many individuals struggle to understand speech in listening scenarios that includereverberation and background noise. An individual’s ability to understand speech arisesfrom a combination of peripheral auditory function, central auditory function, and generalcognitive abilities. The interaction of these factors complicates the prescription oftreatment or therapy to improve hearing function. Damage tothe auditory peripherycan be studied in animals; however, this method alone is not enough to understandthe impact of hearing loss on speech perception. Computational auditory models bridgethe gap between animal studies and human speech perception.Perturbations to themodeled auditory systems can permit mechanism-based investigations into observedhuman behavior. In this study, we propose a computational model that accounts forthe complex interactions between different hearing damagemechanisms and simulateshuman speech-in-noise perception. The model performs a digit classification task asa human would, with only acoustic sound pressure as input. Thus, we can use themodel’s performance as a proxy for human performance. This two-stage model consistsof a biophysical cochlear-nerve spike generator followed by a deep neural network(DNN) classifier. We hypothesize that sudden damage to the periphery affects speechperception and that central nervous system adaptation overtime may compensatefor peripheral hearing damage. Our model achieved human-like performance acrosssignal-to-noise ratios (SNRs) under normal-hearing (NH) cochlear settings, achieving50% digit recognition accuracy at−20.7 dB SNR. Results were comparable to eightNH participants on the same task who achieved 50% behavioralperformance at−22dB SNR. We also simulated medial olivocochlear reflex (MOCR)and auditory nervefiber (ANF) loss, which worsened digit-recognition accuracy at lower SNRs comparedto higher SNRs. Our simulated performance following ANF loss is consistent withthe hypothesis that cochlear synaptopathy im, United States. Department of Defense. Research and Engineering (Air Force Contract No. FA8702-15-D-0001), National Institutes of Health (U.S.) (T32 Trainee Grant No. 5T32DC000038-27), National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant DGE1745303)