Automatic discovery of features underlying the perception of voicing

Responses of both human and animal listeners to synthetic stop-consonant/vowel stimuli in which voice-onset time (VOT) is uniformly varied are known to be `categorical´ but an explanation of this phenomenon remains elusive. A `composite´ model consisting of a physiologically-realistic auditory model feeding its patterns of neural firing to an artificial neural network is described. When trained by (supervised) error back-propagation on the extreme, end-points of the VOT continuum, the composite model is capable of reproducing closely listeners´ behaviour in classical categorical-perception (CP) studies. However, whether the model also reproduces the so-called boundary-shift phenomenon-whereby the phoneme boundary moves with place of articulation-apparently depends upon the precise details of the auditory model and so, by implication, upon subtle aspects of peripheral auditory processing. A first attempt at unsupervised training has been unsuccessful: the likely reason for this is outlined. It is anticipated that future work comparing the model´s responses for unsupervised versus supervised training will help to elucidate the mechanisms of categorical perception