Indicators of Allophony and Phonemehood

Although we are only able to distinguish between a finite, small number of sound categories--i.e. a given language's phonemes--no two sounds are actually identical in the messages we receive. Given the pervasiveness of sound-altering processes across languages--and the fact that every language relies on its own set of phonemes--the question of the acquisition of allophonic rules by infants has received a considerable amount of attention in recent decades. How, for example, do English-learning infants discover that the word forms [kaet] and [kat] refer to the same animal species (i.e. cat), whereas [kaet] and [baet] (i.e. cat ~ bat) do not? What kind of cues may they rely on to learn that [siŋkiŋ] and [θiŋkiŋ] (i.e. sinking ~ thinking) can not refer to the same action? The work presented in this dissertation builds upon the line of computational studies initiated by Peperkamp et al. (2006), wherein research efforts have been concentrated on the definition of sound-to-sound dissimilarity measures indicating which sounds are realizations of the same phoneme. We show that solving Peperkamp et al.'s task does not yield a full answer to the problem of the discovery of phonemes, as formal and empirical limitations arise from its pairwise formulation. We proceed to circumvent these limitations, reducing the task of the acquisition of phonemes to a partitioning-clustering problem and using multidimensional scaling to allow for the use of individual phones as the elementary objects. The results of various classification and clustering experiments consistently indicate that effective indicators of allophony are not necessarily effective indicators of phonemehood. Altogether, the computational results we discuss suggest that allophony and phonemehood can only be discovered from acoustic, temporal, distributional, or lexical indicators when--on average--phonemes do not have many allophones in a quantized representation of the input.