Abstract-It is difficult for some children to pronounce some phonemes such as vowels. In order to improve their pronunciation, this can be done by a human being such as teacher or parents. However, it is difficult to discover the error in the pronunciation without talking with each student individually. With a large number of students in classes nowadays, it is difficult for teachers to communicate with students separately. Therefore, this study proposes an automatic speech recognition system which has the capacity to detect the incorrect phoneme pronunciation. This system can automatically support children to improve their pronunciation by directly asking children to pronounce a phoneme and the system can tell them if it is correct or not. In the future, the system can give them the correct pronunciation and let them practise until they get the correct pronunciation. In order to construct this system, an experiment was done to collect the speech database. In this experiment 89, elementary school children were asked to produce 28 Arabic phonemes 10 times. The collected database contains 890 utterances for each phoneme. For each utterance, fundamental frequency f0, the first 4 formants are extracted and 13 MFCC co-efficients were extracted for each frame of the speech signal. Then 7 statics were applied for each signal. These statics are (max, min, range, mean, mead, variance and standard divination) therefore for each utterance to have 91 features. The second step is to evaluate if the phoneme is correctly pronounced or not using human subjects. In addition, there are six classifiers applied to detect if the phoneme is correctly pronounced or not by using the extracted acoustic features. The experimental results reveal that the proposed method is effective for detecting the miss pronounced phoneme .)"أ"(
This paper presents an automatic system of neural networks (NNs) that has the ability to simulate and predict many of applied problems. The system architectures are automatically reorganized and the experimental process starts again, if the required performance is not reached. This processing is continued until the performance obtained. This system is first applied and tested on the two spiral problem; it shows that excellent generalization performance obtained by classifying all points of the two-spirals correctly. After that, it is applied and tested on the shear stress and the pressure drop problem across the short orifice die as a function of shear rate at different mean pressures for linear low-density polyethylene copolymer (LLDPE) at 190• C. The system shows a better agreement with an experimental data of the two cases: shear stress and pressure drop. The proposed system has been also designed to simulate other distributions not presented in the training set (predicted) and matched them effectively.
& This article presents two systems that have the ability to simulate and predict the proton-proton (P-P) interaction. They are an adaptive neurofuzzy inference system (ANFIS) and a neural networks (NNs) system. The P-P-based ANFIS and NNs models calculate the multiplicity distribution of charged particles at different high energies. Simulation results of training charged particles using the ANFIS and NNs as tested with training data points showed perfect fitting to the experimental data. Prediction capabilities of the ANFIS and NNs checked with data points not used in training also proved to perform well. The results amply demonstrate the feasibility of these techniques in extracting the collision features and prove their effectiveness. It is found that the ANFIS shows better performance and trained more quickly than the NNs system.
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