Enhancing phoneme recogniser performance with a simple rule-based language model

Pertti Väyrynen, Johannes Peltola, Tapio Seppänen

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


    The phoneme classification inaccuracy at the acoustic phonetic level is a major weakness in most speech recognition systems. However, the inaccuracy will violate phonotactic constraints at the acoustic phonetic level. A better performance is expected if a language model is adopted in a recognition system for post-processing phoneme estimates and making corrections with a set of explicit rules of the language used. We developed a simple language model with a set of rules to correct phoneme classification errors made by a Hidden Markov Model based phoneme recognizer. The experimental results indicate that about 20% of
    recognition errors can be corrected.
    Original languageEnglish
    Title of host publicationSTeP2000 Millennium of Artificial Intelligence
    Subtitle of host publicationAl of Tommorrow
    Number of pages7
    Publication statusPublished - 2000
    MoE publication typeA4 Article in a conference publication
    Event9th Finnish Artificial Intelligence Conference : Millennium of Artificial Intelligence - Espoo, Finland
    Duration: 28 Aug 200030 Aug 2000
    Conference number: 9


    Conference9th Finnish Artificial Intelligence Conference
    Abbreviated titleSTeP 2000


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