  Aphasia, a language disorder resulting from brain damage, requires accurate
identification of specific aphasia types, such as Broca's and Wernicke's
aphasia, for effective treatment. However, little attention has been paid to
developing methods to detect different types of aphasia. Recognizing the
importance of analyzing co-speech gestures for distinguish aphasia types, we
propose a multimodal graph neural network for aphasia type detection using
speech and corresponding gesture patterns. By learning the correlation between
the speech and gesture modalities for each aphasia type, our model can generate
textual representations sensitive to gesture information, leading to accurate
aphasia type detection. Extensive experiments demonstrate the superiority of
our approach over existing methods, achieving state-of-the-art results (F1
84.2\%). We also show that gesture features outperform acoustic features,
highlighting the significance of gesture expression in detecting aphasia types.
We provide the codes for reproducibility purposes.
