Eight-legged Robot; Simscape Multibody Toolbox; Optimization; Genetic Algorithm.
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| Title: | Eight-legged Robot; Simscape Multibody Toolbox; Optimization; Genetic Algorithm. |
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| Authors: | A.Rasheed, Abdalem1 alem12@uomosul.edu.iq |
| Source: | Iraqi Journal for Electrical & Electronic Engineering. Jun2026, Vol. 22 Issue 1, p77-85. 9p. |
| Subjects: | Discrete cosine transforms, Principal components analysis, Voiceprints, Automatic speech recognition |
| Abstract (English): | Speaker recognition refers to identifying the speaker by his or her voice. People talk in a variety of tones and each speaking voice has features that distinguish one person from another. Speaker verification (SV)involves comparing a set of measures of the speaker’s utterances with a reference for the person whose identification is being asserted to accept or reject the speaker’s identity claim. An identity claim is made during speaker verification which consists of two steps: extraction of feature and matching of feature. In this work, the analysis of correlations of Mel-scale coefficients for the voice of utterance to identify the intended speaker is presented. Short text-dependent word and other text-independent word is represented in this study. The correlation accuracy ranged from 98% to 99% for user1 (same speaker) for text-dependent. whereas 83% and 61% for user1 correlation with other speakers for text-dependent and independent respectively. Furthermore, the MFCC feature extraction approach based on distributed Discrete Cosine Transform (DCT) is provided in this research. SV tests are carried out using the MFCC feature extractions method where close variance for the target speaker and away variance for other speakers is obtained. Additionally, the principle component analysis (PCA) is provided to improve the discriminative system performance. Where the PCA chooses the optimal path between every pair of extremely confusing speakers. The results obtained from PCA were similar to the correlation finding from the Mel-scale results with enhancing the discriminative information and with lowering the dimension of MFCCs data.. [ABSTRACT FROM AUTHOR] |
| Abstract (Arabic): | يركز المقال على التحقق من المتحدث (التعرف على المتحدث) باستخدام معاملات ميل التردد السيفسترالية (MFCC) وتحليل الارتباط لتمييز المتحدثين بناءً على مقاطع كلامية قصيرة. يعرض النظام استخراج ميزات MFCC من تسجيلات الصوت وتطبيق تحليل الارتباط والتحليل بالمكونات الرئيسية (PCA) لتعزيز التمييز بين المتحدث المقصود والمتحدثين الآخرين. تظهر النتائج التجريبية دقة ارتباط عالية تتراوح بين 98% و99% للتحقق النصي المعتمد على نفس المتحدث، وانخفاضًا ملحوظًا في الارتباط يتراوح بين 61% و83% مع متحدثين آخرين، مما يشير إلى فعالية التمييز بين المتحدثين. كما توضح الدراسة أن تحليل المكونات الرئيسية يقلل من أبعاد الميزات مع الحفاظ على المعلومات التمييزية، مما يحسن أداء التحقق. وتشير المقارنات مع الأبحاث القائمة إلى أن النهج المقترح يحقق دقة تنافسية في ظروف مختبرية منخفضة الضوضاء. [Extracted from the article] |
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| Database: | Engineering Source |
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