Modeling pronunciation variation with context-dependent articulatory feature decision trees

Sam Bowman, Karen Livescu

    Research output: Contribution to conferencePaper

    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)
    Pages326-329
    Number of pages4
    StatePublished - 2010
    Event11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan
    Duration: Sep 26 2010Sep 30 2010

    Other

    Other11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010
    CountryJapan
    CityMakuhari, Chiba
    Period9/26/109/30/10

    Keywords

    • Articulatory features
    • Pronunciation modeling

    ASJC Scopus subject areas

    • Language and Linguistics
    • Speech and Hearing

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  • Cite this

    Bowman, S., & Livescu, K. (2010). Modeling pronunciation variation with context-dependent articulatory feature decision trees. 326-329. Paper presented at 11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010, Makuhari, Chiba, Japan.