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Analysis 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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 Publication date 2016
and research's language is العربية
 Created by Shamra Editor




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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.


Artificial intelligence review:
Research summary
تتناول هذه الدراسة تحليل خوارزميتي MFCC وEndpoint ومدى تأثيرهما على نسب التعرف على الصوت. تتضمن عملية التعرف على الصوت قسمين رئيسيين: التعرف على الكلام والتعرف على المتكلم. تم تصميم نظام للتعرف على المتكلم وأوامره الصوتية باستخدام عدة خوارزميات مكملة. تم إجراء دراسة تحليلية لخوارزمية MFCC التي تستخدم لاستخراج السمات الصوتية، مع التركيز على تأثير عدد المرشحات في بنك المرشحات وعدد السمات المأخوذة من كل إطار على نسب التعرف. كما تم استخدام الشبكات العصبية ذات التغذية الأمامية والانتشار الخلفي للخطأ (FFBPNN) كمصنف، وتم تحليل أداء الشبكة للوصول إلى أفضل خصائص ومكونات لتحقيق عملية التعرف. بالإضافة إلى ذلك، تمت دراسة خوارزمية Endpoint المستخدمة لإزالة فترات الصمت وتأثيرها على نسب التعرف على الصوت. توصلت الدراسة إلى أن زيادة عدد المرشحات في بنك المرشحات وعدد السمات المأخوذة من كل إطار يؤدي إلى تحسين نسب التعرف حتى حد معين، وبعد ذلك تثبت النسب. تم الحصول على أعلى نسبة تعرف قدرها 90.74% عند التعرف على الأوامر الصوتية و87.50% عند التعرف على المتكلم. توصي الدراسة باستخدام بنك مرشحات مكون من عدد مرشحات بين 24-35 واختيار عدد سمات أصغر من عدد المرشحات بقليل لتحقيق أفضل نتائج في التعرف على الصوت.
Critical review
دراسة نقدية: تعتبر هذه الدراسة خطوة مهمة في مجال التعرف على الصوت، حيث تقدم تحليلاً دقيقاً لخوارزميتي MFCC وEndpoint وتأثيرهما على نسب التعرف. ومع ذلك، يمكن تحسين الدراسة من خلال توسيع قاعدة البيانات المستخدمة لتشمل مجموعة أكبر من المتكلمين والأوامر الصوتية، مما يزيد من دقة النتائج وموثوقيتها. كما يمكن دراسة تأثير خوارزميات أخرى لإزالة فترات الصمت ومقارنتها بخوارزمية Endpoint المستخدمة في هذه الدراسة. بالإضافة إلى ذلك، يمكن تحسين الدراسة من خلال تحليل تأثير الضوضاء البيئية على نسب التعرف وتقديم حلول للتعامل معها. بشكل عام، تعتبر الدراسة قيمة وتقدم نتائج مفيدة، ولكن يمكن تحسينها من خلال توسيع نطاق البحث وتحليل المزيد من العوامل المؤثرة على نسب التعرف.
Questions related to the research
  1. ما هي الخوارزميات التي تم تحليلها في الدراسة؟

    تم تحليل خوارزميتي MFCC وEndpoint في الدراسة.

  2. ما هي أعلى نسبة تعرف تم تحقيقها في الدراسة؟

    تم تحقيق أعلى نسبة تعرف قدرها 90.74% عند التعرف على الأوامر الصوتية و87.50% عند التعرف على المتكلم.

  3. ما هو تأثير زيادة عدد المرشحات في بنك المرشحات على نسب التعرف؟

    زيادة عدد المرشحات في بنك المرشحات تؤدي إلى تحسين نسب التعرف حتى حد معين، وبعد ذلك تثبت النسب.

  4. ما هي التوصيات التي قدمتها الدراسة لتحسين نسب التعرف؟

    توصي الدراسة باستخدام بنك مرشحات مكون من عدد مرشحات بين 24-35 واختيار عدد سمات أصغر من عدد المرشحات بقليل لتحقيق أفضل نتائج في التعرف على الصوت.


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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