Learning foreign vowels

Two hypotheses have recently been put forward to account for listeners' ability to distinguish and learn contrasts between speech sounds in foreign languages. First, Best's Perceptual Assimilation Model and Flege's Speech Learning Model both predict that the ease with which a listener can tell one non-native phoneme from another varies directly with the extent to which these sounds assimilate to different native phonemes (Best, 1994; also Best, McRoberts, & Goodell, 2001; Flege, 1991). Second, Logan, Lively, & Pisoni (1991) have argued that training listeners to identify non-native phonemes teaches them sets of exemplars rather than more abstract distinctive feature values. I report here the results of three sets of experiments designed to test these hypotheses, in which American English listeners were trained to categorize German nonlow vowels. The first set of experiments show that some instances of the same contrast between German vowels are more easily discriminated than others, a result incompatible with the predictions of either Best's or Flege's models, but compatible with the alternative category recognition interpretation. The second set of experiments reveals effects of contextual and speaker variation on listeners' ability to learn [tense] but not [high] contrasts between foreign vowels, and are thus at least partly compatible with an exemplar model of foreign category learning (Pisoni, Lively, & Logan, 1994; also Nosofsky, 1986). The third set of experiments compares the predictions of Nosofsky's (1986) selective attention exemplar model of category learning with those of a feature learning model in tests of listeners' learning the natural classes to which the German vowels belong. The results are mixed: listeners learned the features that define the natural classes of [+/- high] and [+/- back] vowels, but could have learned either the feature that defines the natural classes of [+/- tense] vowels or sets of [+/- tense] exemplars. Natural classes defined by abstract distinctive feature values are thus learnable, even if their membership is phonetically polymorphous.