Probabilistic Prediction for Text-Prompted Speaker Verification Capable of Accepting Spoken Words with the Same Meaning but Different Pronunciations

  • Shota Sakashita
  • Satoshi Takeguchi
  • Kazuya Matsuo
  • Shuichi KurogiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)


So far, we have presented a method of probabilistic prediction using GEBI (Gibbs-distribution based Bayesian inference) for flexible text-prompted speaker verification. For more flexible and practical verification, this paper presents a method of verification capable of accepting spoken words with the same meaning but different pronunciations. For example, Japanese language has different pronunciations for a digit, such as /yon/ and /shi/ for 4, /nana/ and /shichi/ for 7, which are usually uttered via unintentional selection, and then it is a practical problem in speech verification of words involving digits, such as ID numbers. With several assumptions, we present a modification of GEBI for dealing with such words. By means of numerical experiments using recorded real speech data, we examine the properties of the present method and show the validity and the effectiveness.


Probabilistic prediction Text-prompted speaker verification Gibbs-distribution-based extended Bayesian inference Words with the same meaning but different pronunciations 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Shota Sakashita
    • 1
  • Satoshi Takeguchi
    • 1
  • Kazuya Matsuo
    • 1
  • Shuichi Kurogi
    • 1
    Email author
  1. 1.Kyushu Institute of TechnologyKitakyushuJapan

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