The Induction of Phonotactics for Speech Segmentation
Converging evidence from computational and human learners
During the first year of life, infants start to learn various properties of their native language. Among these properties are phonotactic constraints, which state the permissible sound sequences within the words of the language. Such constraints guide infants’ search for words in continuous speech, thereby facilitating the development of the mental lexicon. An intriguing problem is how infants are able to acquire knowledge of phonotactics.
This dissertation proposes a computational model of phonotactic learning, which is based on psycholinguistic findings. The model connects two learning mechanisms: statistical learning and feature-based generalization. Using these mechanisms, phonotactic constraints are induced from transcribed utterances of continuous speech, and are subsequently used for the detection of word boundaries. The model is tested in various empirical studies involving computer simulations on transcribed speech data, computer simulations of human segmentation behavior, and artificial language learning experiments with human learners. The dissertation demonstrates that phonotactics can be learned from continuous speech by combining mechanisms that have been shown to be available to language learners. Together, the mechanisms of statistical learning and generalization allow for the induction of a set of phonotactic constraints with varying levels of abstraction. The computational model provides a better account of speech segmentation than models that rely solely on statistical learning. With respect to human learning capacities, the dissertation shows that adult learners can induce novel phonotactic constraints from a continuous speech stream from an artificial language.
By combining computational modeling with psycholinguistic experiments, this dissertation contributes to our understanding of the mechanisms involved in early language acquisition.