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Gauss karışım modeli tabanlı konuşmacı belirleme sistemlerinde klasik MAP uyarlanması yönteminin performans analizi

İsim	Gauss karışım modeli tabanlı konuşmacı belirleme sistemlerinde klasik MAP uyarlanması yönteminin performans analizi
Yazar	Erdoğan, A., Demiroğlu, Cenk
Basım Tarihi:	2010
Basım Yeri	- IEEE
Konu	Gaussian processes, Noise, Speaker recognition
Tür	Belge
Dil	Türkçe
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2165-0608
Kayıt Numarası	451aeeba-209f-4b7b-8905-d46ccd498cfc
Lokasyon	Electrical & Electronics Engineering
Tarih	2010
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Gaussian mixture models (GMM) is one of the most commonly used methods in text-independent speaker identification systems. In this paper, performance of the GMM approach has been measured with different parameters and settings. Voice activity detection (VAD) component has been found to have a significant impact on the performance. Therefore, VAD algorithms that are robust to background noise have been proposed. Significant differences in performance have been observed between male and female speakers and GSM/PSTN channels. Moreover, single-stream GMM approach has been found to perform significantly better than the multi-stream GMM approach. It has been observed under all conditions that data duration is critical for good performance.
DOI	10.1109/SIU.2010.5651366

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Özyeğin Üniversitesi

Gauss karışım modeli tabanlı konuşmacı belirleme sistemlerinde klasik MAP uyarlanması yönteminin performans analizi

Yazar Erdoğan, A., Demiroğlu, Cenk

Basım Tarihi 2010

Basım Yeri - IEEE

Konu Gaussian processes, Noise, Speaker recognition

Tür Belge

Dil Türkçe

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2165-0608

Kayıt Numarası 451aeeba-209f-4b7b-8905-d46ccd498cfc

Lokasyon Electrical & Electronics Engineering

Tarih 2010

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

Örnek Metin Gaussian mixture models (GMM) is one of the most commonly used methods in text-independent speaker identification systems. In this paper, performance of the GMM approach has been measured with different parameters and settings. Voice activity detection (VAD) component has been found to have a significant impact on the performance. Therefore, VAD algorithms that are robust to background noise have been proposed. Significant differences in performance have been observed between male and female speakers and GSM/PSTN channels. Moreover, single-stream GMM approach has been found to perform significantly better than the multi-stream GMM approach. It has been observed under all conditions that data duration is critical for good performance.

DOI 10.1109/SIU.2010.5651366