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2015 | OriginalPaper | Chapter

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

Authors : Shuichi Kurogi, Shota Sakashita, Satoshi Takeguchi, Takuya Ueki, Kazuya Matsuo

Published in: Neural Information Processing

Publisher: Springer International Publishing

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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.

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Literature
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Metadata
Title
Probabilistic Prediction in Multiclass Classification Derived for Flexible Text-Prompted Speaker Verification
Authors
Shuichi Kurogi
Shota Sakashita
Satoshi Takeguchi
Takuya Ueki
Kazuya Matsuo
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26531-5

Electronic ISBN: 978-3-319-26532-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26532-2_24

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