Speech impairment indicative of Alzheimer's Disease (AD) is detected from an input speech sample using a classification model comprising a fine-tuned, pre-trained Bidirectional Encoder Representations from Transformer (BERT)-based model and a classification layer, the fine-tuning using training data comprising transcribed speech, a portion of the training data identified as associated with AD and a portion of the training data identified as not associated with AD. The input need not include acoustic or temporal features. In some implementations, the classification model is augmented through the use of syntactic and word-content features extracted from the speech sample, which are concatenated with an aggregate transcript representation from the BERT-based model and provided to the classification layer.
SYSTEM AND METHOD FOR ALZHEIMER'S DISEASE DETECTION FROM SPEECH