Gauss karışım modeli tabanlı konuşmacı doğrulama sistemlerinde kişiye ve kanala uyarlanmada klasik MAP tabanlı yöntemlerin performans analizi

Name: Gauss karışım modeli tabanlı konuşmacı doğrulama sistemlerinde kişiye ve kanala uyarlanmada klasik MAP tabanlı yöntemlerin performans analizi
Author: Koşunda, Serol, Yeşil, Fatih, Ayazoğlu, Yaprak, Demiroğlu, Cenk

İsim	Gauss karışım modeli tabanlı konuşmacı doğrulama sistemlerinde kişiye ve kanala uyarlanmada klasik MAP tabanlı yöntemlerin performans analizi
Yazar	Koşunda, Serol, Yeşil, Fatih, Ayazoğlu, Yaprak, Demiroğlu, Cenk
Basım Tarihi:	2011
Basım Yeri	- IEEE
Konu	Gaussian processes, Eigenvalues and eigenfunctions, Maximum likelihood estimation, Speaker recognition
Tür	Belge
Dil	Türkçe
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4577-0462-8
Kayıt Numarası	cb97dc66-2573-4f41-abdc-49dda4b88662
Lokasyon	Electrical & Electronics Engineering
Tarih	2011
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	In this paper, performance of Gaussian mixture models (GMM) based algorithms implemented in Speech Processing Laboratory at Ozyegin University, within NIST SRE2004 and 2006 database was reported. Gaussian mixture models (GMM) is one of the most commonly used methods in text-independent speaker verification systems. In this paper, performance of the GMM approach has been measured with different parameters and settings. It has also been observed that eigenchannel-MAP and JFA methods both have increased the performance of the system against session variability which is one of the most challenging problem in text-independent speaker verification systems.
DOI	10.1109/SIU.2011.5929804

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Gauss karışım modeli tabanlı konuşmacı doğrulama sistemlerinde kişiye ve kanala uyarlanmada klasik MAP tabanlı yöntemlerin performans analizi

Yazar Koşunda, Serol, Yeşil, Fatih, Ayazoğlu, Yaprak, Demiroğlu, Cenk

Basım Tarihi 2011

Basım Yeri - IEEE

Konu Gaussian processes, Eigenvalues and eigenfunctions, Maximum likelihood estimation, Speaker recognition

Tür Belge

Dil Türkçe

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4577-0462-8

Kayıt Numarası cb97dc66-2573-4f41-abdc-49dda4b88662

Lokasyon Electrical & Electronics Engineering

Tarih 2011

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin In this paper, performance of Gaussian mixture models (GMM) based algorithms implemented in Speech Processing Laboratory at Ozyegin University, within NIST SRE2004 and 2006 database was reported. Gaussian mixture models (GMM) is one of the most commonly used methods in text-independent speaker verification systems. In this paper, performance of the GMM approach has been measured with different parameters and settings. It has also been observed that eigenchannel-MAP and JFA methods both have increased the performance of the system against session variability which is one of the most challenging problem in text-independent speaker verification systems.

DOI 10.1109/SIU.2011.5929804