  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.
