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On the impact of using optimization search method in large speaker database based on hybrid modeling over experimental investigation

Ahmad, Abdul Manan and Loh, Mun Yee (2008) On the impact of using optimization search method in large speaker database based on hybrid modeling over experimental investigation. Jurnal Teknologi Maklumat, 20 (3). pp. 158-172. ISSN 0128-3790

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Abstract

Speaker recognition from speech signal is still an ongoing research in forensics and biometrics area. Speaker recognition is the process to enable machine to recognize speaker's identity from their speech. Recent development on classify speaker data from a group of speaker is still insufficient to provide a satisfied result in achieving high performance pattern classification engine. There are two main difficulties in this field: how to maintain accuracy rate under incremental amounts of training data and how to reduce the time processing in the case embedded systems need to consider about efficient and simplicity of calculation. Recently we have proposed three difference hybrid pattern classification approach for text independent speaker identification system; in these approaches, we combined a hybrid GMMNQ and decision Tree model. The aim of this paper is to show the progress of the development of a high impact hybrid modeling. Besides, via this paper, an evaluation is done to verify the impact of using optimization search method on large speaker database.

Item Type:Article
Additional Information:Special issue in computer science
Uncontrolled Keywords:speaker identification system, gaussian mixture model, vector quantization, hybrid vector quantization/gaussian mixture model
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System
ID Code:11021
Deposited By: Zalinda Shuratman
Deposited On:19 Nov 2010 02:46
Last Modified:01 Nov 2017 04:17

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