The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo apresenta um método de verificação de locutor independente de texto usando modelos de mistura gaussiana (GMMs), onde apenas enunciados de falantes inscritos são necessários. Coortes artificiais são usadas em vez daquelas de bancos de dados de falantes, e GMMs para coortes artificiais são gerados alterando os parâmetros do modelo do GMM para um falante reivindicado. As taxas de erro iguais pelo método proposto são cerca de 60% menores do que as de um método convencional que também utiliza apenas declarações de falantes inscritos.
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Yuuji MUKAI, Hideki NODA, Michiharu NIIMI, Takashi OSANAI, "Text-Independent Speaker Verification Using Artificially Generated GMMs for Cohorts" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 10, pp. 2536-2539, October 2008, doi: 10.1093/ietisy/e91-d.10.2536.
Abstract: This paper presents a text-independent speaker verification method using Gaussian mixture models (GMMs), where only utterances of enrolled speakers are required. Artificial cohorts are used instead of those from speaker databases, and GMMs for artificial cohorts are generated by changing model parameters of the GMM for a claimed speaker. Equal error rates by the proposed method are about 60% less than those by a conventional method which also uses only utterances of enrolled speakers.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.10.2536/_p
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@ARTICLE{e91-d_10_2536,
author={Yuuji MUKAI, Hideki NODA, Michiharu NIIMI, Takashi OSANAI, },
journal={IEICE TRANSACTIONS on Information},
title={Text-Independent Speaker Verification Using Artificially Generated GMMs for Cohorts},
year={2008},
volume={E91-D},
number={10},
pages={2536-2539},
abstract={This paper presents a text-independent speaker verification method using Gaussian mixture models (GMMs), where only utterances of enrolled speakers are required. Artificial cohorts are used instead of those from speaker databases, and GMMs for artificial cohorts are generated by changing model parameters of the GMM for a claimed speaker. Equal error rates by the proposed method are about 60% less than those by a conventional method which also uses only utterances of enrolled speakers.},
keywords={},
doi={10.1093/ietisy/e91-d.10.2536},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Text-Independent Speaker Verification Using Artificially Generated GMMs for Cohorts
T2 - IEICE TRANSACTIONS on Information
SP - 2536
EP - 2539
AU - Yuuji MUKAI
AU - Hideki NODA
AU - Michiharu NIIMI
AU - Takashi OSANAI
PY - 2008
DO - 10.1093/ietisy/e91-d.10.2536
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2008
AB - This paper presents a text-independent speaker verification method using Gaussian mixture models (GMMs), where only utterances of enrolled speakers are required. Artificial cohorts are used instead of those from speaker databases, and GMMs for artificial cohorts are generated by changing model parameters of the GMM for a claimed speaker. Equal error rates by the proposed method are about 60% less than those by a conventional method which also uses only utterances of enrolled speakers.
ER -