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Alzheimers disease is a fatal progressive brain disorder that worsens withtime. It is high time we have inexpensive and quick clinical diagnostictechniques for early detection and care. In previous studies, various MachineLearning techniques and Pre-trained Deep Learning models have been used inconjunction 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) 2021Challenge. We use the well-balanced dataset provided by the ADReSS Challengefor training and validating our models. Model 1 uses various acoustic featuresfrom the eGeMAPs feature-set, Model 2 uses various linguistic features that wegenerated from auto-generated transcripts and Model 3 uses the auto-generatedtranscripts directly to extract features using a Pre-trained BERT and TF-IDF.These models are described in detail in the models section.

Alzheimers Dementia Detection using Acoustic & Linguistic features and Pre-Trained BERT