A comparison of syntax, semantics, and pragmatics in spoken language among residents with Alzheimer´s disease in managed-care facilities

This research is a discriminative analysis of conversational dialogues involving individuals suffering from dementia of Alzheimer´s type. Several metric analyses are applied to the transcripts of the Carolina Conversation Corpus in order to determine if there are significant statistical differences between individuals with and without Alzheimer´s disease. Our prior research suggests that there exist measurable linguistic differences between managed-care residents diagnosed with Alzheimer´s disease and their caregivers. This paper presents results comparing managed-care residents diagnosed with Alzheimer´s disease to other managed-care residents. Results from the analysis indicate that part-of-speech and lexical richness statistics may not be good distinguishing attributes. However, go-ahead utterances and certain fluency measures provide defensible means of differentiating the linguistic characteristics of spontaneous speech between individuals that are and are not diagnosed with Alzheimer´s disease. Two machine learning algorithms were able to classify the speech of individuals with and without dementia of the Alzheimer´s type with accuracy up to 80%.