  This paper describes a multi-modal approach for the automatic detection of
Alzheimer's disease proposed in the context of the INESC-ID Human Language
Technology Laboratory participation in the ADReSS 2020 challenge. Our
classification framework takes advantage of both acoustic and textual feature
embeddings, which are extracted independently and later combined. Speech
signals are encoded into acoustic features using DNN speaker embeddings
extracted from pre-trained models. For textual input, contextual embedding
vectors are first extracted using an English Bert model and then used either to
directly compute sentence embeddings or to feed a bidirectional LSTM-RNNs with
attention. Finally, an SVM classifier with linear kernel is used for the
individual evaluation of the three systems. Our best system, based on the
combination of linguistic and acoustic information, attained a classification
accuracy of 81.25%. Results have shown the importance of linguistic features in
the classification of Alzheimer's Disease, which outperforms the acoustic ones
in terms of accuracy. Early stage features fusion did not provide additional
improvements, confirming that the discriminant ability conveyed by speech in
this case is smooth out by linguistic data.
