The seeming ease with which we usually understand each other belies the complexity of the processes that underlie speech perception. One of the biggest computational challenges is that different talkers realize the same speech categories (e.g., /p/) in physically different ways. We review the mixture of processes that enable robust speech understanding across talkers despite this lack of invariance. These processes range from automatic pre-speech adjustments of the distribution of energy over acoustic frequencies (normalization) to implicit statistical learning of talker-specific properties (adaptation, perceptual recalibration) to the generalization of these patterns across groups of talkers (e.g., gender differences).
