  Introduction: Alzheimer's disease is a type of dementia in which early
diagnosis plays a major rule in the quality of treatment. Among new works in
the diagnosis of Alzheimer's disease, there are many of them analyzing the
voice stream acoustically, syntactically or both. The mostly used tools to
perform these analysis usually include machine learning techniques. Objective:
Designing an automatic machine learning based diagnosis system will help in the
procedure of early detection. Also, systems, using noninvasive data are
preferable. Methods: We used are classification system based on spoken
language. We use three (statistical and neural) approaches to classify audio
signals from spoken language into two classes of dementia and control. Result:
This work designs a multi-modal feature embedding on the spoken language audio
signal using three approaches; N-gram, i-vector, and x-vector. The evaluation
of the system is done on the cookie picture description task from Pitt Corpus
dementia bank with the accuracy of 83:6
