Automatically identifying trouble-indicating speech behaviors in alzheimer's disease

Alzheimer's disease (AD) deteriorates executive, linguistic, and functional capacity and is rapidly becoming more prevalent. In particular, AD leads to an inability to follow simple dialogues. In this paper, we annotate two databases of dyad conversations, that include individuals with AD, with trouble indicating behaviors (TIBs). We then extract lexical/syntactic and acoustic features from all utterances and identify those that are most indicative of TIB (which include speech rate and utterance likelihoods in a standard language model) and classify utterances as having TIB or not with up to 79.5% accuracy. This will allow us to build automated dialogue systems and assessment tools that are sensitive to confusion in people with AD.