A dysphonia, or disorder of the mechanisms of phonation in the larynx, can create time-varying amplitude fluctuations in the voice. A model for band-dependent analysis of this amplitude modulation (AM) phenomenon in dysphonic speech is developed from a traditional communications engineering perspective. This perspective challenges current dysphonia analysis methods that analyze AM in the time-domain signal. An automatic dysphonia recognition system is designed to exploit AM in voice using a biologically-inspired model of the inferior colliculus. This system, built upon a Gaussian-mixture-model (GMM) classification backend, recognizes the presence of dysphonia in the voice signal. Recognition experiments using data obtained from the Kay Elemetrics Voice Disorders Database suggest that the system provides complementary information to state-of-the-art mel-cepstral features. We present dysphonia recognition as an approach to developing features that capture glottal source differences in normal speech.