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Performance analysis of the classical MAP adaptation method in Gaussian mixture model based speaker detection systems

Title	Performance analysis of the classical MAP adaptation method in Gaussian mixture model based speaker detection systems
Author	Erdoğan, A., Demiroğlu, Cenk
Publication Date:	2010
Publication Place	- IEEE
Subject	Gaussian processes, Noise, Speaker recognition
Type	Document
Language	Turkish
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2165-0608
Record ID	451aeeba-209f-4b7b-8905-d46ccd498cfc
Library Location	Electrical & Electronics Engineering
Date	2010
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	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 University

Performance analysis of the classical MAP adaptation method in Gaussian mixture model based speaker detection systems

Author Erdoğan, A., Demiroğlu, Cenk

Publication Date 2010

Publication Place - IEEE

Subject Gaussian processes, Noise, Speaker recognition

Type Document

Language Turkish

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2165-0608

Record ID 451aeeba-209f-4b7b-8905-d46ccd498cfc

Library Location Electrical & Electronics Engineering

Date 2010

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

Sample Text 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