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


    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.
    Number of pages12
    ISBN (Print)3540615326, 9783540615323
    Publication statusPublished - 1 Jan 1996
    EventPacific Rim International Conference on Artificial Intelligence 1996 - Cairns, Australia
    Duration: 26 Aug 199630 Aug 1996
    Conference number: 4th
    https://link.springer.com/book/10.1007/3-540-61532-6 (Proceedings)

    Publication series

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


    ConferencePacific Rim International Conference on Artificial Intelligence 1996
    Abbreviated titlePRICAI 1996
    Internet address

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