ABSTRACTp laying has become an integral part of people's lives since the beginning of time, and education games have become an important part of the education process in childhood, for school students and even for university students. Insertion of the voice commands in education games considered a big challenge especially regarding the speech accuracy and rapid response, to achieve this goal an educational game was designed aimed to teach students of Computer Science the fundamental concepts of " logic ", and to enable the game to allow speech input, the game should include the speech recognition system, to build that system, in this study three algorithms for feature extraction are used (MFCC , PLP and Rasta-PLP) with three VQ Code Book generation algorithms (LBG, LBG-PSO and LBG-PSOGA) were studied and applied, and was tested on 864 sound files for different peoples (4 male, 5 female), their ages between (16-30) year, through the results it was noted that when MFCC technique with LBG-PSOGA algorithm was used higher speech accuracy up to 98.5 % was obtained compared to other algorithms and techniques.
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%.
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