ASR Systems as Models of Phonetic Category Perception in Adults

We test the potential of standard Automatic Speech Recognition (ASR) systems trained on large corpora of continuous speech as quantitative models of human speech processing. In human adults, speech perception is attuned to efficiently process native speech sounds, at the expense of difficulties in pro- cessing non-native sounds. We use ABX-discriminability measures to test whether ASR models can account for the patterns of confusion between speech sounds observed in humans. We show that ASR models reproduce some well-documented effects in non-native phonetic perception. Beyond the immediate results, our methodology opens up the possibility of a more systematic investigation of phonetic category perception in humans.