  Alzheimer's disease (AD) constitutes a neurodegenerative disease with serious
consequences to peoples' everyday lives, if it is not diagnosed early since
there is no available cure. Alzheimer's is the most common cause of dementia,
which constitutes a general term for loss of memory. Due to the fact that
dementia affects speech, existing research initiatives focus on detecting
dementia from spontaneous speech. However, little work has been done regarding
the conversion of speech data to Log-Mel spectrograms and Mel-frequency
cepstral coefficients (MFCCs) and the usage of pretrained models. Concurrently,
little work has been done in terms of both the usage of transformer networks
and the way the two modalities, i.e., speech and transcripts, are combined in a
single neural network. To address these limitations, first we represent speech
signal as an image and employ several pretrained models, with Vision
Transformer (ViT) achieving the highest evaluation results. Secondly, we
propose multimodal models. More specifically, our introduced models include
Gated Multimodal Unit in order to control the influence of each modality
towards the final classification and crossmodal attention so as to capture in
an effective way the relationships between the two modalities. Extensive
experiments conducted on the ADReSS Challenge dataset demonstrate the
effectiveness of the proposed models and their superiority over
state-of-the-art approaches.
