Text dependent speaker identification system based on deep learning | ||
| Journal of Education and Science | ||
| Article 11, Volume 30, Issue 4, September 2021, Pages 141-160 PDF (1.11 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/edusj.2021.130144.1161 | ||
| Authors | ||
| Qasim Sadiq Mahmood* ; Yusra Faisal Al-Irahyim | ||
| جامعة الموصل-کلیة علوم الحاسوب والریاضیات- قسم علوم الحاسوب | ||
| Abstract | ||
| Speaker identification techniques are one of those most advanced modern technologies and there are many different systems had been developed, from methods that used to extract characteristics and classification. The applications of Speech identification are quite difficult and requires modern technologies with a large number of audio samples and resources. In this research, the system of speaker identification had been designed based on a text (the word or sentences are pre-defined) which give the system the capability to identify the speaker in the least time, number of training samples and resources. The system consists four main parts, the first one is to create audio databases. In the study, two audio databases were relied upon, the first being a database (QS- Dataset) and the second database (audioMNIST_meta). The databases were processed and configured in a way that was explained in the body of the research later. The second part of the research is to extract the characteristics through the pitch coefficients algorithm, while the third part is the use of the neural network as a classifier. And the last part of the research is to verify the work and results of the system. The test results showed the ability of the MNN network to deal with the smallest number of data, as it achieved a percentage of 100%. As for large data, it ranged from 80% to 81%. Unlike CNN network, the results were not good for the few data, from 60% to 76%, and with large data it was The results are excellent, from 91% to 96%. | ||
| Keywords | ||
| Speaker identification,,; ,،,؛MFCC,,; ,،,؛Multilayer Neural Network,,; ,،,؛convolution neural network,,; ,،,؛Deep Learning | ||
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