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Register a new userAnalysis study about (MFCC and Endpoint) algorithms and the extent of their impact in voice recognition rates
دراسة تحليلية لخوارزميتي ( MFCC و ENDPOINT) و مدى تأثيرهما في نسب التعرف على الصوت
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دعد يوسف الكعدي( باحث )
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يشتمل التعرف على الصوت قسمين أساسيين و هما التعرف على الكلام و التعرف على المتكلم، حيث تعد عمليات التعرف هذه من أهم التقنيات الحديثة و قد تم تطوير العديد من الأنظمة التي تختلف بالطرق المستخدمة في استخراج السمات و طرق التصنيف لتدعم أنظمة تعرف من هذا النوع. اشتملت الدراسة في هذا البحث على القسمين السابقين، حيث تم تصميم نظام تعرف على المتكلم و أوامره الصوتية و استخدام عدة خوارزميات متكاملة لإنجاز البحث. قمنا بإجراء دراسة تحليلية لخوارزمية Mel Frequency Cepstral Coefficients ((MFCC المستخدمة في استخراج السمات، و تمت دراسة بارامترين خاصين بهذه الخوارزمية هما عدد المرشحات في بنك المرشحات و عدد السمات المأخوذة من كل إطار و علاقة هذين البارامترين ببعضهما و مدى تأثير قيمتهما على نسب التعرف. و تم استخدام الشبكات العصبية ذات التغذية الأمامية و الانتشار الخلفي للخطأ Forwarding back propagation Neural Networks (FFBPNN)Feed كمصنف و حللنا أداء الشبكة للوصول إلى أفضل خصائص و مكونات محققة عملية التعرف. كما تمت دراسة خوارزمية Endpoint المستخدمة لإزالة فترات الصمت و تأثيرها في نسب التعرف على الصوت.
Voice recognition includes two basic parts: speech and speaker recognition. These recognition processes consider as the most important processes of modern technologies, many systems has been developed that differ in the methods used to extract features and classification ways to support recognition systems of this type. The study was conducted in this research on the previous subject, where the system is designed to recognize the speaker and his voice orders and focus on several complementary algorithms to carry out the research. we conducted an analytical study on MFCC algorithm used in the extraction of features, and it has been studying two parameters the number of filters in the filters bank and the number of features that taken from each frame and the impact of these two parameters in the recognition rate and the relationship of these two parameters on each other. It was the use of feed forwarding back propagation neural networks performance analysis as characteristics and we analyze the performance of the network to gain access to the best features and components to the process of achieving recognition. And it has been studying Endpoint algorithm that used to remove periods of silence and its impact on voice recognition rates.
References used
CARROLL, T.;COLANGELO, R.;STROTT, T."Bird Call Identifier –Identifying Songs of Bird Species through Digital Signal Processing Techniques". 2010,118
Xue, X."Joint Speech and Speaker Recognition Using Neural Networks".NOVIA-University of applied science. 2013,60
(CHOUDHARY, A.;KSHIRSAGAR,R."Process Speech Recognition System using Artificial Intelligence Technique".(IJSCE) ,ISSN: 2231-2307, Volume-2, Issue-5, 2012,PP(239-242
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