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Listeners form average-based representations of individual voice identities.
Models of voice perception propose that identities are encoded relative to an abstracted average or prototype. While there is some evidence for norm-based coding when learning to discriminate different voices, little is ...
Breaking voice identity perception: Expressive voices are more confusable for listeners.
The human voice is a highly flexible instrument for self-expression, yet voice identity perception is largely studied using controlled speech recordings. Using two voice-sorting tasks with naturally varying stimuli, we ...
The effects of high variability training on voice identity learning.
High variability training has been shown to benefit the learning of new face identities. In three experiments, we investigated whether this is also the case for voice identity learning. In Experiment 1a, we contrasted high ...