  Collecting and accessing a large amount of medical data is very
time-consuming and laborious, not only because it is difficult to find specific
patients but also because it is required to resolve the confidentiality of a
patient's medical records. On the other hand, there are deep learning models,
trained on easily collectible, large scale datasets such as Youtube or
Wikipedia, offering useful representations. It could therefore be very
advantageous to utilize the features from these pre-trained networks for
handling a small amount of data at hand. In this work, we exploit various
multi-modal features extracted from pre-trained networks to recognize
Alzheimer's Dementia using a neural network, with a small dataset provided by
the ADReSS Challenge at INTERSPEECH 2020. The challenge regards to discern
patients suspicious of Alzheimer's Dementia by providing acoustic and textual
data. With the multi-modal features, we modify a Convolutional Recurrent Neural
Network based structure to perform classification and regression tasks
simultaneously and is capable of computing conversations with variable lengths.
Our test results surpass baseline's accuracy by 18.75%, and our validation
result for the regression task shows the possibility of classifying 4 classes
of cognitive impairment with an accuracy of 78.70%.
