@inproceedings{oai:kyutech.repo.nii.ac.jp:00004371,
 author = {Kurogi, Shuichi and 黒木, 秀一 and Sakashita, Shota and Takeguchi, Satoshi and Ueki, Takuya and Matsuo, Kazuya and 松尾, 一矢},
 book = {Lecture Notes in Computer Science},
 month = {Nov},
 note = {So far, we have presented amethod for text-promptedmultistep 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 inverence) and show the effectiveness and the risk of probability loss in the present method., 22nd International Conference on Neural Information Processing, ICONIP 2015, November 9-12, 2015, Istanbul, Turkey},
 pages = {216--225},
 publisher = {Springer, Cham},
 title = {Probabilistic Prediction in Multiclass Classification Derived for Flexible Text-Prompted Speaker Verification},
 volume = {9489},
 year = {2015},
 yomi = {クロギ, シュウイチ and マツオ, カズヤ}
}