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Probabilistic Prediction in Multiclass Classification Derived for Flexible Text-Prompted Speaker Verification

  • Shuichi KurogiEmail author
  • Shota Sakashita
  • Satoshi Takeguchi
  • Takuya Ueki
  • Kazuya Matsuo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

Abstract

So far, we have presented a method for text-prompted multistep speaker verification using GEBI (Gibbs-distribution based extended Bayesian inference) for reducing single-step verification error, where we use thresholds for acceptance and rejection but the tuning is not so easy and affects the performance of verification. To solve the problem of thresholds, this paper presents a method of probabilistic prediction in multiclass classification for solving verification problem. We also present loss functions for evaluating the performance of probabilistic prediction. By means of numerical experiments using recorded real speech data, we examine the properties of the present method using GEBI and BI (Bayesian inference) and show the effectiveness and the risk of probability loss in the present method.

Keywords

Probabilistic prediction Text-prompted speaker verification Gibbs-distribution-based extended Bayesian inference Loss functions in multiclass classification 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Shuichi Kurogi
    • 1
    Email author
  • Shota Sakashita
    • 1
  • Satoshi Takeguchi
    • 1
  • Takuya Ueki
    • 1
  • Kazuya Matsuo
    • 1
  1. 1.Kyushu Institute of TechnologyKitakyushu, FukuokaJapan

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