In this paper, we report on a study with the aim of automatically detecting phoneme-level mispronunciations in 32 French speakers suffering from unilateral facial palsy at four different clinical severity grades. We sought to determine if the Goodness of Pronunciation (GOP) algorithm, which is commonly used in Computer-Assisted Language Learning systems to detect learners' individual errors, could also detect segmental deviances in disordered speech. For this purpose, speech read by the 32 speakers was aligned and GOP scores were computed for each phone realization. The highest scores, which indicate large dissimilarities with standard phone realizations, were obtained for the most severely impaired speakers. The corresponding speech subset was manually transcribed at phone-level. 8.3% of the phones differed from standard pronunciations extracted from our lexicon. The GOP technique allowed to detect 70.2% of mispronunciations with an equal rate of about 30% of false rejections and false acceptances. The phone substitutions detected by the algorithm confirmed that some of the speakers have difficulties to produce bilabial plosives, and showed that other sounds such as sibilants are prone to mispronunciation. Another interesting finding was the fact that speakers diagnosed with a same pathology grade do not necessarily share the same pronunciation issues.