  Alzheimer's dementia (AD) affects memory, thinking, and language,
deteriorating person's life. An early diagnosis is very important as it enables
the person to receive medical help and ensure quality of life. Therefore,
leveraging spontaneous speech in conjunction with machine learning methods for
recognizing AD patients has emerged into a hot topic. Most of the previous
works employ Convolutional Neural Networks (CNNs), to process the input signal.
However, finding a CNN architecture is a time-consuming process and requires
domain expertise. Moreover, the researchers introduce early and late fusion
approaches for fusing different modalities or concatenate the representations
of the different modalities during training, thus the inter-modal interactions
are not captured. To tackle these limitations, first we exploit a Neural
Architecture Search (NAS) method to automatically find a high performing CNN
architecture. Next, we exploit several fusion methods, including Multimodal
Factorized Bilinear Pooling and Tucker Decomposition, to combine both speech
and text modalities. To the best of our knowledge, there is no prior work
exploiting a NAS approach and these fusion methods in the task of dementia
detection from spontaneous speech. We perform extensive experiments on the
ADReSS Challenge dataset and show the effectiveness of our approach over
state-of-the-art methods.
