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Alzheimer's Disease (AD) is a neurodegenerative condition characterized by linguistic impairments. While ASR and LLMs show promise in AD detection, ASR often normalizes key AD-related speech patterns and faces cross-lingual challenges due to language dependencies. Besides, ASR training demands extensive matched data. In our paper, however, we employ a phoneme recognizer as a frontend tokenizer. Provided it has comprehensive phoneme coverage, a multitude of linguistic phenomena can be represented via phoneme sequences, including hesitations, repetitions, pauses, mispronunciations, and even distinctions between different language identities that are crucial for AD detection. Furthermore, the BERT model is employed to extract high-dimensional features from the Phonetic PosteriorGrams (PPGs), which are ultimately used to diagnose Alzheimer's disease. Our approach offers cross-lingual applicability, achieves competitive accuracy, and maintains computational efficiency.