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Speech modules for enhancement of computer aided pronunciation learning system / Mustafa Abdullah Elhosiny Abdullah ; Supervised Mohsen A. Rashawn , Mohamed A. Elgamal

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mustafa Abdullah El-Hosiny Abdullah , 2016Description: 63 P. : charts , facsimiles ; 30cmOther title:
  • تحسين كفاءة نظام حاسوبى لتعليم النطق السليم باستخدام نماذج صوتية [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Mathematics and Physics Summary: Computer aided pronunciation learning (CAPL) has recently been considerable in the research community. One of the most challenging applications of a (CAPL) system is Holy Quran training for correct recitation. Gaussian mixture models (GMMS) have been the most common used models in pronunciation verification systems. The recently introduced deep neural networks (DNN) has proved to provide significantly better discriminating models of the acoustic space. In this thesis, we carried out a large number of experiments to achieve a significant improvement in the accuracy of Speech Verification system. A hybrid deep neural network-hidden markov models (DNN-HMM) approach is used for that purpose. Also, an automatic manner for selecting the training data and transcribing it was developed. As a result, we can select a sufficient part of that data with high confidence and building a very strong computer Aiding system for Holy Quran. In this work, we implemented a confidence-based scheme for automatic selection of data. This scheme is basically based on the forward backward algorithm that utilizes log-posterior probabilities. This scheme can select up to around 30% of data with accuracy reaches to 95.5%
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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.2016.Mu.S (Browse shelf(Opens below)) Not for loan 01010110071468000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.10.M.Sc.2016.Mu.S (Browse shelf(Opens below)) 71468.CD Not for loan 01020110071468000

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

Computer aided pronunciation learning (CAPL) has recently been considerable in the research community. One of the most challenging applications of a (CAPL) system is Holy Quran training for correct recitation. Gaussian mixture models (GMMS) have been the most common used models in pronunciation verification systems. The recently introduced deep neural networks (DNN) has proved to provide significantly better discriminating models of the acoustic space. In this thesis, we carried out a large number of experiments to achieve a significant improvement in the accuracy of Speech Verification system. A hybrid deep neural network-hidden markov models (DNN-HMM) approach is used for that purpose. Also, an automatic manner for selecting the training data and transcribing it was developed. As a result, we can select a sufficient part of that data with high confidence and building a very strong computer Aiding system for Holy Quran. In this work, we implemented a confidence-based scheme for automatic selection of data. This scheme is basically based on the forward backward algorithm that utilizes log-posterior probabilities. This scheme can select up to around 30% of data with accuracy reaches to 95.5%

Issued also as CD

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