  Cognitive decline is a natural process that occurs as individuals age. Early
diagnosis of anomalous decline is crucial for initiating professional treatment
that can enhance the quality of life of those affected. To address this issue,
we propose a multimodal model capable of predicting Mild Cognitive Impairment
and cognitive scores. The TAUKADIAL dataset is used to conduct the evaluation,
which comprises audio recordings of clinical interviews. The proposed model
demonstrates the ability to transcribe and differentiate between languages used
in the interviews. Subsequently, the model extracts audio and text features,
combining them into a multimodal architecture to achieve robust and generalized
results. Our approach involves in-depth research to implement various features
obtained from the proposed modalities.
