Determination of mispronunciations and ensuring feedback to users are maintained by computer-assisted language learning (CALL) systems. In this work, we introduce an ensemble model that defines the mispronunciation of Arabic phonemes and assists learning of Arabic, effectively. To the best of our knowledge, this is the very first attempt to determine the mispronunciations of Arabic phonemes employing ensemble learning techniques and conventional machine learning models, comprehensively. In order to observe the effect of feature extraction techniques, mel-frequency cepstrum coefficients (MFCC), and Mel spectrogram are blended with each learning algorithm. To show the success of proposed model, 29 letters in the Arabic phonemes, 8 of which are hafiz, are voiced by a total of 11 different person. The amount of data set has been enhanced employing the methods of adding noise, time shifting, time stretching, pitch shifting. Extensive experiment results demonstrate that the utilization of voting classifier as an ensemble algorithm with Mel spectrogram feature extraction technique exhibits remarkable classification result with 95.9% of accuracy.

翻译：计算机辅助语言学习(CALL)系统维持了错误发音的确定和确保用户反馈。 在这项工作中,我们引入了一个混合模型,用以界定阿拉伯语电话的错误发音,并有效地协助学习阿拉伯语。根据我们的最佳知识,这是首次尝试用共同学习技术和常规机器学习模式全面确定阿拉伯电话的错误发音。为了观察地物提取技术的影响,将Mel-频率 Cepstrum系数(MFCC)和Mel光谱仪与每一项学习算法混合在一起。为了显示拟议的模型的成功,共有11个不同的人表示阿拉伯电话中29个字母,其中8个是hafiz。数据集的数量已经通过增加噪音、时间变化、时间拉长、轮椅移动等方法得到加强。广泛的实验结果表明,使用投票分类器作为混合算法与Mel光谱特征提取技术的混合算法(MFCC)的效果是惊人的,精确度达95.9%。