{"pk":31305,"title":"Lerning Distributed Representations for Syllables","subtitle":null,"abstract":"This paper presents a connectionist model of how representations for syllables might be learned from sequences of phones. A simple recurrent network is trained to distinguish a set of words in an artificial language, which are presented to it as sequences of phonetic feature vectors. The distributed syllable representations that are learned as a side-effect of this task are used as input to other networks. It is shown that these representations encode syllable structure in a way which permits the regeneration of the phone sequences (for production) as well as systematic phonological operations on the representations.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[],"section":"Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/6g10h23r","frozenauthors":[{"first_name":"Michael","middle_name":"","last_name":"Gasser","name_suffix":"","institution":"Indiana University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"1992-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/31305/galley/22374/download/"}]}