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{
    "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/"
        }
    ]
}