Alzheimer’s disease, a subset of dementia, causes a decrease in cognitive ability, decreasing the patient’s ability to perform common tasks such as accessing memories and communicating with others. Constant work is done to provide a sound cure for Alzheimer’s, yet these cures are only as effective as the methods used to detect Alzheimer’s and dementia in early stages. With the current methods of detection only finding 50% of those with the illness (Shinkawa et al., 2018), more research needs to be done so that the expected 150 million Dementia patients by 2050 will have longer, happier lives. Current Alzheimer’s detection methods typically are based on the detection of biomarkers known to be associated with the disease. MRI and PET scans are some of the more common testing methods (Yang et al., 2022). While extremely accurate, the manpower and cost involved in carrying out such tests make them hard to access, especially in locations where healthcare systems are not strong. Another testing method is the collection of cerebrospinal fluid. Whilst cheaper, this method is very invasive and thus undesirable for the patient (Yang et al., 2022). Machine learning models that can detect Alzheimer’s through speech have also been explored. This method is much cheaper to scale, but the high level of data collection required has restricted this method in many studies to being used in one language, which is often English (Pérez-Toro et al., 2023). In English alone, databases containing Alzheimer’s or Dementia patients and healthy controls need to be ethically collected to perform these tests, with enough data for models to be accurate. The high bar of entry is especially detrimental to smaller communities that may share a tribal, regional, or uncommon language. 6909 languages exist currently, making training an individual model for each language a lofty goal (Anderson, n.d.). A model could be developed to incorporate universal features of similar languages, or of any language with training data, to provide tools for diagnosis in a more diverse band of languages.