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Post-traumatic Stress Disorder (PTSD) is a mental condition that develops as a result of catastrophic events. Triggers for this may include experiences, such as military combat, natural disasters, or sexual abuse, having a great influence on the mental wellbeing. Due to the severity of this condition, early detection and professional treatment is crucial. For this reason, previous research explored prediction models for recognising PTSD at an early stage. However, when these models are transferred from research to real-world applications, they face heterogeneous environments (e.g., different recording settings, various dialects or languages). To analyse this effect, we develop a speech-based PTSD recognition model and subsequently analyse its cross-corpus and cross-linguistic performance. Our experiments indicate that there are cross-cultural factors incluencing PTSD and leading to a best area under the ROC curve (AUC) of 70.1% evaluated on the cross-corpus.