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Subvocal speech recognition using engineered features and deep learning / Mohamed Said Elbially Elmahdy ; Supervised Ahmed A. Morsy

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mohamed Said Elbially Elmahdy , 2017Description: 77 P. : facsimiles ; 30cmOther title:
  • التعرف على الكلام الغير مسموع من خلال خصائص الصوت و التعلم العميق [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering Summary: In this study we propose an end-to-end deep system for subvocal speech recognition. A single channel surface electromyogram (sEMG) placed diagonally around the throat is used alongside a close-talk microphone for signal acquisition. The system was tested on a corpus of 20 words. The system classification was independent of the word level but smart enough to learn the mapping function from sound and sEMG sequences to letters, then extracting the most probable word from these letters. Different input signals and different depth levels were investigated using the deep learning model. The system was tested on ten healthy subjects (5 females, 5 males). The proposed system achieved a word error rate (WER) of 9.44, 8.44 and 9.22 for speech, speech combined with single channel sEMG and speech with two channels of sEMG, respectively. In order to compare the system with the results from literature, a wide range of hand crafted features were extracted and tested with Support Vector machine (SVM) and K-Nearest Neighbors. Results were comparable to those reported in literature
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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 Cai01.13.03.M.Sc.2017.Mo.S (Browse shelf(Opens below)) Not for loan 01010110073632000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.03.M.Sc.2017.Mo.S (Browse shelf(Opens below)) 73632.CD Not for loan 01020110073632000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

In this study we propose an end-to-end deep system for subvocal speech recognition. A single channel surface electromyogram (sEMG) placed diagonally around the throat is used alongside a close-talk microphone for signal acquisition. The system was tested on a corpus of 20 words. The system classification was independent of the word level but smart enough to learn the mapping function from sound and sEMG sequences to letters, then extracting the most probable word from these letters. Different input signals and different depth levels were investigated using the deep learning model. The system was tested on ten healthy subjects (5 females, 5 males). The proposed system achieved a word error rate (WER) of 9.44, 8.44 and 9.22 for speech, speech combined with single channel sEMG and speech with two channels of sEMG, respectively. In order to compare the system with the results from literature, a wide range of hand crafted features were extracted and tested with Support Vector machine (SVM) and K-Nearest Neighbors. Results were comparable to those reported in literature

Issued also as CD

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