The auditory brainstem response (ABR) is a powerful neurophysiological measure to diagnose hearing deficits along the auditory pathway. Wave I of the ABRs is particularly critical for assessing early hearing loss, though hard to observe in humans. The major downside of ABR is that most protocols are very boring since they use thousands of clicks to elicit ABRs. Here, we derived modeled ABRs with continuous speech from an audiobook. Unlike other studies involving computationally intensive modification that made their speech stimuli unnatural-sounding and unlikely to be used in real-life applications, we applied a fast and efficient algorithm that enhances speech transients to better elicit ABRs. Using the auditory periphery model that simulates human brains, we derived ABRs from our transient speech and showed a significantly larger Wave I-V ratio compared to other stimuli. These results demonstrated a potential of assessing hearing conditions in a more objective and naturalistic way.