A Research on the Stochastic Model for Spoken Language Understanding

  • Yong-Wan Roh
  • Kwang-Seok Hong
  • Hyon-Gu Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3043)


In this paper, we propose a new stochastic model for sentence speech understanding using dictionary and thesaurus. The proposed model searches the dictionary for the same word with input text. If it is not in the dictionary, the proposed model search the high level words in the high level word dictionary based on the thesaurus. We compare the probability of sentence understanding model with threshold probability, and we’ll get the sentence understanding rate. We evaluated the performance of the sentence speech understanding system by applying twenty questions game. As the experiment results, we got sentence speech understanding accuracy of 79.8%. In this case probability (α) of high level word is 0.9 and threshold probability (β) is 0.38.


Recognition Rate Speech Recognition Threshold Probability Understanding System Speech Recognizer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yong-Wan Roh
    • 1
  • Kwang-Seok Hong
    • 1
  • Hyon-Gu Lee
    • 2
  1. 1.School of Information and Communication EngineeringSungkyunkwan UniversityJangan-gu, SuwonKorea
  2. 2.Information and Communication EngineeringSeoil Junior CollegeSeoulKorea

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