Computer-Assisted Language Learning (CALL) software can provide complementary second language learning perspectives when used together with conventional classroom-based training (Farr & Liam, 2016). The Faculty of Translation and Interpreting, University of Geneva, has developed a multilingual Open Source CALL platform, CALL-SLT, based on speech recognition and language processing (Rayner & al., 2014). Research studies conducted on CALL-SLT support that its frequent use resulted in a statistically significant improvement in the second language oral and written competencies (Bouillon & al., 2011a; Baur, 2015; Dejos & al., 2016). Our experience investigates the usefulness and acceptability of CALL-SLT among Arabic speaking asylum seekers and refugees who are attending beginner French as a Foreign Language's (FFL) classes in an associative environment in Geneva. 80 sentences were displayed in Arabic to four participants. Each had to find their French equivalent and orally record them, using or not audio and written help. Quantitative results showed that 60% of the sentences have been correctly recognized by CALL-SLT, mostly after the second attempt. Participants filled out a post-test questionnaire to give their feedback. Our findings reveal that CALL-SLT is well-received among the field of asylum as a FFL's learning tool, and helps beginner learners to improve pronunciation and vocabulary.