The acquisition of allophonic rules: statistical learning with linguistic constraints. Peperkamp, S., Calvez, R. L., Nadal, J., & Dupoux, E. Cognition, 101(3):B31-41, 2006.
doi  abstract   bibtex   
Phonological rules relate surface phonetic word forms to abstract underlying forms that are stored in the lexicon. Infants must thus acquire these rules in order to infer the abstract representation of words. We implement a statistical learning algorithm for the acquisition of one type of rule, namely allophony, which introduces context-sensitive phonetic variants of phonemes. This algorithm is based on the observation that different realizations of a single phoneme typically do not appear in the same contexts (ideally, they have complementary distributions). In particular, it measures the discrepancies in context probabilities for each pair of phonetic segments. In Experiment 1, we test the algorithm's performances on a pseudo-language and show that it is robust to statistical noise due to sampling and coding errors, and to non-systematic rule application. In Experiment 2, we show that a natural corpus of semiphonetically transcribed child-directed speech in French presents a very large number of near-complementary distributions that do not correspond to existing allophonic rules. These spurious allophonic rules can be eliminated by a linguistically motivated filtering mechanism based on a phonetic representation of segments. We discuss the role of a priori linguistic knowledge in the statistical learning of phonology.
@Article{Peperkamp2006,
  author   = {Sharon Peperkamp and Rozenn Le Calvez and Jean-Pierre Nadal and Emmanuel Dupoux},
  journal  = {Cognition},
  title    = {The acquisition of allophonic rules: statistical learning with linguistic constraints.},
  year     = {2006},
  number   = {3},
  pages    = {B31-41},
  volume   = {101},
  abstract = {Phonological rules relate surface phonetic word forms to abstract
	underlying forms that are stored in the lexicon. Infants must thus
	acquire these rules in order to infer the abstract representation
	of words. We implement a statistical learning algorithm for the acquisition
	of one type of rule, namely allophony, which introduces context-sensitive
	phonetic variants of phonemes. This algorithm is based on the observation
	that different realizations of a single phoneme typically do not
	appear in the same contexts (ideally, they have complementary distributions).
	In particular, it measures the discrepancies in context probabilities
	for each pair of phonetic segments. In Experiment 1, we test the
	algorithm's performances on a pseudo-language and show that it is
	robust to statistical noise due to sampling and coding errors, and
	to non-systematic rule application. In Experiment 2, we show that
	a natural corpus of semiphonetically transcribed child-directed speech
	in French presents a very large number of near-complementary distributions
	that do not correspond to existing allophonic rules. These spurious
	allophonic rules can be eliminated by a linguistically motivated
	filtering mechanism based on a phonetic representation of segments.
	We discuss the role of a priori linguistic knowledge in the statistical
	learning of phonology.},
  doi      = {10.1016/j.cognition.2005.10.006},
  keywords = {Humans, Linguistics, Models, Phonetics, Statistical, Verbal Learning, 16364279},
}

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