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

arXiv: https://arxiv.org/abs/2109.11010 · HTML: https://mlsanity.com/paper/2109.11010/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2008.01551/) (2020)
- [Exploiting Multi-Modal Features From Pre-trained Networks for Alzheimer's Dementia Recognition](https://mlsanity.com/paper/2009.04070/) (2020)
- [Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity](https://mlsanity.com/paper/2009.00700/) (2020)
- [Multi-modal fusion with gating using audio, lexical and disfluency features for Alzheimer's Dementia recognition from spontaneous speech](https://mlsanity.com/paper/2106.09668/) (2021)
- [The INESC-ID Multi-Modal System for the ADReSS 2020 Challenge](https://mlsanity.com/paper/2005.14646/) (2020)
- [Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speech](https://mlsanity.com/paper/2006.07358/) (2020)

## Built on by (most cited first, 4 of 4)

- [Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses](https://mlsanity.com/paper/2306.03443/) (2023)
- [Large Language Models for Bioinformatics](https://mlsanity.com/paper/2501.06271/) (2025)
- [Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0](https://mlsanity.com/paper/2501.16201/) (2025)
- [Reverse-Speech-Finder: A Neural Network Backtracking Architecture for Generating Alzheimer's Disease Speech Samples and Improving Diagnosis Performance](https://mlsanity.com/paper/2505.17477/) (2025)

## Abstract (arXiv)

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.