In today’s interconnected world, humans live in a closely connected community, where it is essential to acquire proficiency in multiple languages to interact with others effectively. Therefore, research in computer-assisted language learning (CALL) is a dynamic study area, focusing on pronunciation mastery as the most challenging aspect. Pronunciation assessment is a keystone in computer-assisted pronunciation teaching (CAPT) systems. It aims to detect mispronunciations and provide informative feedback to learners. This task has been approached as a classification problem. Herein, the amount of available data is of great importance during the training stage. However, datasets are more likely to be imbalanced. This paper tackles the issue of imbalanced datasets by suggesting a semi-supervised approach using the one-class classification (OCC) method. We trained a convolutional neural network (CNN) to detect mispronounced words, whilst the CNN is exclusively trained based on well-pronounced ones. The experiments were conducted on the freely accessible Arabic speech mispronunciation detection dataset (ASMDD). The obtained results show an accuracy of about 84% on unseen pronunciations.