Modeling pronunciation variation with context-dependent articulatory feature decision trees

Sam Bowman, Karen Livescu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We consider the problem of predicting the surface pronunciations of a word in conversational speech, using a model of pronunciation variation based on articulatory features. We build context-dependent decision trees for both phone-based and feature-based models, and compare their perplexities on conversational data from the Switchboard Transcription Project. We find that a fully-factored model, with separate decision trees for each articulatory feature, does not perform well, but a feature-based model using a smaller number of "feature bundles" outperforms both the fully-factored model and a phone-based model. The articulatory feature-based decision trees are also much more robust to reductions in training data. We also analyze the usefulness of various context variables.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
    PublisherInternational Speech Communication Association
    Pages326-329
    Number of pages4
    StatePublished - 2010

    Publication series

    NameProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010

    Keywords

    • Articulatory features
    • Pronunciation modeling

    ASJC Scopus subject areas

    • Language and Linguistics
    • Speech and Hearing
    • Human-Computer Interaction
    • Signal Processing
    • Software
    • Modeling and Simulation

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