Learning Allophones from the Input

Perceptual contrast and its consequences play a large role in both first and second language acquisition. Infants acquiring their native language must learn to divide the speech stream into the vowels and consonants that perform a contrastive role in the inventory of their language. Researchers have proposed that infants initially engage in a process of phonetic category learning and subsequently, after exposure to their native language contrasts, begin to unify these phonetic exemplars into more abstract phoneme categories (Pierrehumbert 2003). The objects of this phonetic learning process are the language’s allophones, or contextualized surface variants of the more abstract phoneme categories. The infant learner must determine whether the variation perceived in the speech stream is attributable to allophonic or phonemic contrasts in the language’s sound categories. The mechanism used by children to carry out this task is assumed to involve a statistical analysis of the acoustic space and the grouping together of input tokens to form areas of high density exemplars, which eventually abstract away and become phoneme categories (Pierrehumbert 2003, Werker and Curtin 2005). The precise characterization of this mechanism has yet to be determined, although there is growing evidence in favour of a role for distributional frequencies in the input (Maye & Gerken, 2000; Maye, Werker, Gerken, 2002). While individual tokens of a sound category may differ considerably, all tokens of the same type will be more similar amongst themselves than across different sound categories (the phenomenon of categorical perception) and more similar along specific acoustic dimensions, such as VOT in the case of consonants or F1/F2 values in the case of vowels. Acoustic dimensions that are important for the differentiation of a language’s phonetic categories will serve to organize tokens into a bimodal distribution along this dimension; dimensions which are not informative will lead to the formation of a token cluster at the center of the distribution (Maye, Werker & Gerken 2002) In a series of experiments, Maye, Werker and Gerken (2002) showed that the perception of allophonic contrasts by six and eight month-old infants can be modified after exposure to either bimodal or monomodal distributions of the allophones [d] and the unaspirated voiceless stop [t] in English. Infant participants were exposed to a continuum of eight tokens ranging from [d] – [t] in eight equal VOT steps. These particular contrasts were selected because they do not constitute a phonemic contrast in English and are both perceived as members of the /d/ category when in word initial position. After exposure, infants in the bimodal group performed significantly better (20%) than the monomodal group on a discrimination task, indicating that they had begun to separate the sounds into two categories. There was no effect for age. Their finding indicates that infants at these age groups are in fact sensitive to statistical distributions in the input, which is what one would suppose if in fact they are using this mechanism during the first year of life when native language categories are developing. One of the major advantages of Maye, Werker, & Gerken’s distribution-based learning algorithm is that it precludes the need for a lexicon. A considerable body of research has demonstrated that infants as young as 8-months are able to track statistical regularities in the speech stream after as little as two minutes of exposure (Saffran, Aslin, Newport, 1996), results which indicate that a lexicon is not necessary for the acquisition of sound categories to begin. As Peperkamp, Pettinato & Dupoux (2003) argue a distributional analysis of the input exploits the complementary distribution between phonemes and their allophones whereby pairs of segments whose lists of contexts have an empty intersection are in complementary distribution and occur as allophonic contrasts. These studies suggest that infants have available to them a statistical learning mechanism that can track distributions in the absence of a lexicon. Maye & Gerken (2000) used the same distribution-based training cited above with adult learners, employing the identical allophonic contrasts of [d] and [t]. They exposed adult native English speaker listeners to either the bimodal or monomodal distributions. Their results showed a significant effect for distribution, where the bimodal group scored significantly better than the monomodal group (20%) on the contrasts that included the segments of interest. Is the same statistical mechanism available to adults, and can it track distributions without relying on a lexicon to derive meaningful contrasts? Presumably, adults can make use of lexical contrasts and feedback in the acquisition of minimal pairs, a fact which has been observed in Japanese learners of the /r/ vs. /l/ contrast. McCandliss, Fiez, Protopapas, Conway &McClelland (2002) showed that when accompanied by