  Alzheimers disease is a fatal progressive brain disorder that worsens with
time. It is high time we have inexpensive and quick clinical diagnostic
techniques for early detection and care. In previous studies, various Machine
Learning techniques and Pre-trained Deep Learning models have been used in
conjunction with the extraction of various acoustic and linguistic features.
Our study focuses on three models for the classification task in the ADReSS
(The Alzheimers Dementia Recognition through Spontaneous Speech) 2021
Challenge. We use the well-balanced dataset provided by the ADReSS Challenge
for training and validating our models. Model 1 uses various acoustic features
from the eGeMAPs feature-set, Model 2 uses various linguistic features that we
generated from auto-generated transcripts and Model 3 uses the auto-generated
transcripts directly to extract features using a Pre-trained BERT and TF-IDF.
These models are described in detail in the models section.
