Understanding spoken language involves far more than decoding a linear sequence of phonetic elements. In view of the inherent variability of the acoustic signal in spontaneous speech, it is not entirely clear that the sort of representation derived from locus equations is sufficient to account for the robustness of spoken language understanding under real-world conditions. An alternative representation, based on the low-frequency modulation spectrum, provides a more plausible neural foundation for spoken language processing.