Lexical access using minimum message length encoding

Ian Thomas, Ingrid Zukerman, Jonathan Oliver, Bhavani Raskutti

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    Abstract

    A method for deriving equivalence classes for lexical access in speech recognition is considered, which automatically derives equivalence classes from training data using unsupervised learning and the Minimum Message Length Criterion. These classes model insertions, deletions and substitutions in an input phoneme string due to mis-recognition and mis-pronunciation, and allow unlikely word candidates to be eliminated quickly. This in turn allows a more detailed examination of the remaining candidates to be carried out efficiently.

    Original languageEnglish
    Title of host publicationPRICAI 1996
    Subtitle of host publicationTopics in Artificial Intelligence - 4th Pacific Rim International Conference on Artificial Intelligence, Proceedings
    PublisherSpringer-Verlag London Ltd.
    Pages229-240
    Number of pages12
    ISBN (Print)3540615326, 9783540615323
    DOIs
    Publication statusPublished - 1 Jan 1996
    Event4th Pacific Rim International Conference on Artificial Intelligence, PRICAI 1996 - Cairns, Australia
    Duration: 26 Aug 199630 Aug 1996

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume1114
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference4th Pacific Rim International Conference on Artificial Intelligence, PRICAI 1996
    CountryAustralia
    CityCairns
    Period26/08/9630/08/96

    Cite this

    Thomas, I., Zukerman, I., Oliver, J., & Raskutti, B. (1996). Lexical access using minimum message length encoding. In PRICAI 1996: Topics in Artificial Intelligence - 4th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 229-240). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1114). Springer-Verlag London Ltd.. https://doi.org/10.1007/3-540-61532-6_20
    Thomas, Ian ; Zukerman, Ingrid ; Oliver, Jonathan ; Raskutti, Bhavani. / Lexical access using minimum message length encoding. PRICAI 1996: Topics in Artificial Intelligence - 4th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer-Verlag London Ltd., 1996. pp. 229-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    @inproceedings{26cd6f5db5d1460baef12930261d30ec,
    title = "Lexical access using minimum message length encoding",
    abstract = "A method for deriving equivalence classes for lexical access in speech recognition is considered, which automatically derives equivalence classes from training data using unsupervised learning and the Minimum Message Length Criterion. These classes model insertions, deletions and substitutions in an input phoneme string due to mis-recognition and mis-pronunciation, and allow unlikely word candidates to be eliminated quickly. This in turn allows a more detailed examination of the remaining candidates to be carried out efficiently.",
    author = "Ian Thomas and Ingrid Zukerman and Jonathan Oliver and Bhavani Raskutti",
    year = "1996",
    month = "1",
    day = "1",
    doi = "10.1007/3-540-61532-6_20",
    language = "English",
    isbn = "3540615326",
    series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    publisher = "Springer-Verlag London Ltd.",
    pages = "229--240",
    booktitle = "PRICAI 1996",
    address = "Germany",

    }

    Thomas, I, Zukerman, I, Oliver, J & Raskutti, B 1996, Lexical access using minimum message length encoding. in PRICAI 1996: Topics in Artificial Intelligence - 4th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1114, Springer-Verlag London Ltd., pp. 229-240, 4th Pacific Rim International Conference on Artificial Intelligence, PRICAI 1996, Cairns, Australia, 26/08/96. https://doi.org/10.1007/3-540-61532-6_20

    Lexical access using minimum message length encoding. / Thomas, Ian; Zukerman, Ingrid; Oliver, Jonathan; Raskutti, Bhavani.

    PRICAI 1996: Topics in Artificial Intelligence - 4th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer-Verlag London Ltd., 1996. p. 229-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1114).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    TY - GEN

    T1 - Lexical access using minimum message length encoding

    AU - Thomas, Ian

    AU - Zukerman, Ingrid

    AU - Oliver, Jonathan

    AU - Raskutti, Bhavani

    PY - 1996/1/1

    Y1 - 1996/1/1

    N2 - A method for deriving equivalence classes for lexical access in speech recognition is considered, which automatically derives equivalence classes from training data using unsupervised learning and the Minimum Message Length Criterion. These classes model insertions, deletions and substitutions in an input phoneme string due to mis-recognition and mis-pronunciation, and allow unlikely word candidates to be eliminated quickly. This in turn allows a more detailed examination of the remaining candidates to be carried out efficiently.

    AB - A method for deriving equivalence classes for lexical access in speech recognition is considered, which automatically derives equivalence classes from training data using unsupervised learning and the Minimum Message Length Criterion. These classes model insertions, deletions and substitutions in an input phoneme string due to mis-recognition and mis-pronunciation, and allow unlikely word candidates to be eliminated quickly. This in turn allows a more detailed examination of the remaining candidates to be carried out efficiently.

    UR - http://www.scopus.com/inward/record.url?scp=84957869906&partnerID=8YFLogxK

    U2 - 10.1007/3-540-61532-6_20

    DO - 10.1007/3-540-61532-6_20

    M3 - Conference Paper

    SN - 3540615326

    SN - 9783540615323

    T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    SP - 229

    EP - 240

    BT - PRICAI 1996

    PB - Springer-Verlag London Ltd.

    ER -

    Thomas I, Zukerman I, Oliver J, Raskutti B. Lexical access using minimum message length encoding. In PRICAI 1996: Topics in Artificial Intelligence - 4th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer-Verlag London Ltd. 1996. p. 229-240. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-61532-6_20