Using LSTMs to Assess the Obligatoriness of Phonological Distinctive
Features for Phonotactic Learning
Abstract
To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segmentlevel phonotactic acquisition, we compare the
performance of two recurrent neural network
models of phonotactic learning: one that has
access to distinctive features at the start of
the learning process, and one that does not.
Though the predictions of both models are
significantly correlated with human judgments
of non-words, the feature-naive model significantly outperforms the feature-aware one in
terms of probability assigned to a held-out test
set of English words, suggesting that distinctive features are not obligatory for learning
phonotactic patterns at the segment level