**Brief description:** This project is a reanalysis of https://osf.io/ftmpa. Listeners performed speech recognition with two simultaneous talkers when those talkers uttered identical phrases ("Unison" condition) and when those talkers uttered phrases with different key words ("Competing" condition). Here, we use a Bayesian hierarchical drift diffusion model (HDDM) to determine the regions of the speech modulation power spectrum that support successful speech recognition (HDDM drift rate parameter), and we run a second-stage Bayesian hierarchical model to estimate the association between trial-wise drift rate and trial-wise fMRI amplitude. **Brief methods:** Twenty-five listeners performed a 3-AFC variant of the Coordinate Response Measure task in the Unison and Competing conditions during fMRI scanning. Spectrotemporal modulation filtering was performed on two-talker mixtures using the Bubbles procedure (Venezia et al., 2020) and an HDDM was used to estimate which spectrotemporal modulations affected the model's drift rate parameter, separately for Unison and Competing. Vocal pitch of the target talker modulated drift rate significantly more in Competing than Unison. Trial-wise posterior predictions of drift rate were generated in two ways: (i) 'overall drift rate,' which was allowed to depend on the entire speech modulation spectrum including vocal pitch and phonetic content, and (ii) 'pitch-restricted drift rate,' which was allowed to depend only the vocal pitch of the target talker. A second-state Bayesian hierachical linear model was employed to determine the association of each type of drift rate with brain response in each condition (Unison, Competing). Venezia, Jonathan H., Marjorie R. Leek, and Michael P. Lindeman. "Suprathreshold differences in competing speech perception in older listeners with normal and impaired hearing." Journal of Speech, Language, and Hearing Research 63.7 (2020): 2141-2161. **Manuscript pre-print** at [[add PsyArXiv link]] **Code and Data:** All code used for stimulus generation/presentation and fMRI preprocessing, as well as a full set of example stimuli, can be found at https://osf.io/ftmpa. The files repository included with this project contains code for HDDM and Bayesian brain-behavior analyses. Results of brain-behavior analyses are included in cortical surface format (text files to be mapped to the surface files in 'template_MNI' using SUMA) and HDDM results are included as R data files. Please see the README.txt file for a description of the included code/data. **Contact:** jonathan.venezia@va.gov, christian.herreraortiz@va.gov