In animal societies, identity signals mediate interactions within groups, and allow individuals to discriminate group-mates from out-group competitors. However, individual recognition becomes increasingly challenging as group size increases and as signals must be transmitted over greater distances. Group vocal signatures appear to evolve when successful in-group/out-group distinctions are at the crux of fitness-relevant decisions, but individual-based recognition systems may be favored when differentiated within-group relationships are important for decision-making. Spotted hyenas are social carnivores that live in stable clans of <125 individuals composed of multiple unrelated matrilines. Clan members cooperate to defend resources and communal territories from neighboring clans and other mega carnivores; this collective defense is mediated by long-range (up to 5 km range) recruitment vocalizations, called whoops. Here, we use machine learning to determine that spotted hyena whoops contain individual but not group signatures, and that fundamental frequency features that propagate well are critical for individual discrimination. For effective clan-level cooperation, hyenas face the cognitive challenge of remembering and recognizing individual voices at long range. We show that serial redundancy in whoop bouts increases individual classification accuracy and thus extended call bouts used by hyenas likely evolved to overcome the challenges of communicating individual identity at long distance.