Total: 1
This work explores the potential of Large Language Models (LLMs) as annotators of high-level characteristics of speech transcriptions, which may be relevant for detecting Alzheimer's disease (AD). These low-dimension interpretable features, here designated as macro-descriptors (e.g. text coherence, lexical diversity), are then used to train a binary classifier. Our experiments compared the extraction of these features from both manual and automatic transcriptions obtained with different types of speech recognition systems, and involved both open and closed source LLMs, with several prompting strategies. The experiments also compared the use of macro-descriptors with the direct prediction of AD by the LLM, given the transcription. Even though LLMs are not trained for this task, our experiments show that they achieve up to 81% accuracy, surpassing the baseline of previous AD detection challenges, particularly when used as extractors of macro-descriptors.