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Abstract Several methods have recently been developed to derive the auditory brainstem response (ABR) from continuous natural speech, facilitating investigation into subcortical encoding of speech. These tools rely on deconvolution to compute the temporal response function (TRF), which models the subcortical auditory pathway as a linear system, where a nonlinearly processed stimulus is taken as the input (i.e., regressor), the electroencephalogram (EEG) data as the output, and the ABR as the impulse response deconvolved from the recorded EEG and the regressor. In this study, we analyzed EEG recordings from subjects listening to both unaltered natural speech and synthesized “peaky speech.” We compared the derived ABR TRFs using three regressors: the half-wave rectified stimulus (HWR) from Maddox and Lee (2018), the glottal pulse train (GP) from Polonenko and Maddox (2021), and the auditory nerve modeled response (ANM; Zilany et al. (2014); (2009)) used in Shan et al. (2024). Our evaluation focused on the signal-to-noise ratio, prediction accuracy, efficiency, and practicality of applying each regressor in both unaltered and peaky speech. The results indicate that the ANM regressor with peaky speech provides the best performance, with the ANM for unaltered speech and the GP regressor for peaky speech close behind, whereas the HWR regressor demonstrated relatively poorer performance. There are, thus, multiple stimulus and analysis tools that can provide high-quality subcortical TRFs, with the choices for which to use dictated by experimental needs. The findings in this study will guide future research and clinical use in selecting the most appropriate paradigm for ABR derivation from continuous, naturalistic speech. 

Music and speech are encountered daily and are unique to human beings. Both are transformed by the auditory pathway from an initial acoustical encoding to higher level cognition. Studies of cortex have revealed distinct brain responses to music and speech, but differences may emerge in the cortex or may be inherited from different subcortical encoding. In the first part of this study, we derived the human auditory brainstem response (ABR), a measure of subcortical encoding, to recorded music and speech using two analysis methods. The first method, described previously and acoustically based, yielded very different ABRs between the two sound classes. The second method, however, developed here and based on a physiological model of the auditory periphery, gave highly correlated responses to music and speech. We determined the superiority of the second method through several metrics, suggesting there is no appreciable impact of stimulus class (i.e., music vs speech) on the way stimulus acoustics are encoded subcortically. In this study’s second part, we considered the cortex. Our new analysis method resulted in cortical music and speech responses becoming more similar but with remaining differences. The subcortical and cortical results taken together suggest that there is evidence for stimulus-class dependent processing of music and speech at the cortical but not subcortical level.