Extracting Phonetic Knowledge from Learning Systems: Perceptrons, Support Vector Machines and Linear Discriminants

摘要

Speech perception relies on the human ability to decode continuous, analogue sound pressure waves into discrete, symbolic labels (‘phonemes’) with linguistic meaning. Aspects of this signal-to-symbol transformation have been intensively studied over many decades, using psychophysical procedures. The perception of (synthetic) syllable-initial stop consonants has been especially well studied, since these sounds display a marked categorization effect: they are typically dichotomised into ‘voiced’ and ‘unvoiced’ classes according to their voice onset time (VOT). In this case, the category boundary is found to have a systematic relation to the (simulated) place of articulation, but there is no currently-accepted explanation of this phenomenon. Categorization effects have now been demonstrated in a variety of animal species as well as humans, indicating that their origins lie in general auditory and/or learning mechanisms, rather than in some ‘phonetic module’ specialized to human speech processing.