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Speaker identification using minimum volume ellipsoids and large-margin criterion / Omar Abdallah Abdelfatah Elgendy ; Supervised Abdel-Karim S. O. Hassan , Moataz M. H. Elayadi

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Omar Abdallah Abdelfatah Elgendy , 2015Description: 106 P. : charts ; 30cmOther title:
  • التعرف على المتحدث باستخدام المجسمات الناقصية ذات أقل حجم ومعيار الهامش الكبير [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Mathematics and Physics Summary: This thesis proposes two approaches building robust text-independent speaker identification systems in noisy environments. The first approach is based using robust statistical estimators such as the Minimum Volume Ellipsoid and Minimum Covariance Determinant to improve the maximum likelihood based classifiers by making them robust to outliers. The second approach is based on using an improved version for the large-margin discriminative criterion, which is characterized by its simplicity compared to the standard large-margin approach and other discriminative approaches. Experimental results show that our proposed techniques outperform the baseline techniques
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.10.M.Sc.2015.Om.S (Browse shelf(Opens below)) Not for loan 01010110067702000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.10.M.Sc.2015.Om.S (Browse shelf(Opens below)) 67702.CD Not for loan 01020110067702000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Mathematics and Physics

This thesis proposes two approaches building robust text-independent speaker identification systems in noisy environments. The first approach is based using robust statistical estimators such as the Minimum Volume Ellipsoid and Minimum Covariance Determinant to improve the maximum likelihood based classifiers by making them robust to outliers. The second approach is based on using an improved version for the large-margin discriminative criterion, which is characterized by its simplicity compared to the standard large-margin approach and other discriminative approaches. Experimental results show that our proposed techniques outperform the baseline techniques

Issued also as CD

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