دراسة تحليلية لخوارزميتي ( MFCCو Endpoint) ومدى تأثيرهما في نسب التعرف على الصوت

Authors

  • دعد الكعدي

Abstract

يشتمل التعرف على الصوت قسمين أساسيين وهما التعرف على الكلام والتعرف على المتكلم، حيث تعد عمليات التعرف هذه من أهم التقنيات الحديثة وقد تم تطوير العديد من الأنظمة التي تختلف بالطرق المستخدمة في استخراج السمات وطرق التصنيف لتدعم أنظمة تعرف من هذا النوع. اشتملت الدراسة في هذا البحث على القسمين السابقين، حيث تم تصميم نظام تعرف على المتكلم وأوامره الصوتية واستخدام عدة خوارزميات متكاملة لإنجاز البحث. قمنا بإجراء دراسة تحليلية لخوارزميةMel Frequency Cepstral Coefficients ((MFCC المستخدمة في استخراج السمات، وتمت دراسة بارامترين خاصين بهذه الخوارزمية هما عدد المرشحات في بنك المرشحات وعدد السمات المأخوذة من كل إطار وعلاقة هذين البارامترين ببعضهما ومدى تأثير قيمتهما على نسب التعرف. وتم استخدام الشبكات العصبية ذات التغذية الأمامية والانتشار الخلفي للخطأ Forwarding back propagation Neural Networks (FFBPNN)Feed كمصنف وحللنا أداء الشبكة للوصول إلى أفضل خصائص ومكونات محققة عملية التعرف. كما تمت دراسة خوارزمية Endpoint المستخدمة لإزالة فترات الصمت وتأثيرها 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.

Downloads

Published

2017-05-07

How to Cite

1.
الكعدي د. دراسة تحليلية لخوارزميتي ( MFCCو Endpoint) ومدى تأثيرهما في نسب التعرف على الصوت. Tuj-eng [Internet]. 2017May7 [cited 2025Jan.28];38(2). Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/2762

Issue

Vol. 38 No. 2 (2016): العلوم الهندسية

Section

Articles

License

Copyright (c) 2017 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series

 Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series   .  The Readers have the right to send, print and subscribe to the initial version of the article, and the title of  Tishreen University Journal for Research and Scientific Studies - Engineering Sciences Series     Publisher

journal uses a CC BY-NC-SA license which mean

You are free to:

  • Adapt — remix, transform, and build upon the material
  •  
  • The licensor cannot revoke these freedoms as long as you follow the license terms.

 

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.