Ethological views of brain functioning suggest that sound representations and computations in the auditory neural system are optimized finely to process and discriminate behaviorally relevant acoustic features and sounds (e.g., spectrotemporal modulations in the songs of zebra finches). Here, we show that modeling of neural sound representations in terms of frequency-specific spectrotemporal modulations enables accurate and specific reconstruction of real-life sounds from high-resolution functional magnetic resonance imaging (fMRI) response patterns in the human auditory cortex. Region-based analyses indicated that response patterns in separate portions of the auditory cortex are informative of distinctive sets of spectrotemporal modulations. Most relevantly, results revealed that in early auditory regions, and progressively more in surrounding regions, temporal modulations in a range relevant for speech analysis (∼2–4 Hz) were reconstructed more faithfully than other temporal modulations. In early auditory regions, this effect was frequency-dependent and only present for lower frequencies (<∼2 kHz), whereas for higher frequencies, reconstruction accuracy was higher for faster temporal modulations. Further analyses suggested that auditory cortical processing optimized for the fine-grained discrimination of speech and vocal sounds underlies this enhanced reconstruction accuracy. In sum, the present study introduces an approach to embed models of neural sound representations in the analysis of fMRI response patterns. Furthermore, it reveals that, in the human brain, even general purpose and fundamental neural processing mechanisms are shaped by the physical features of real-world stimuli that are most relevant for behavior (i.e., speech, voice).