# Comparing Acoustic-based Approaches for Alzheimer's Disease Detection

arXiv: https://arxiv.org/abs/2106.01555 · HTML: https://mlsanity.com/paper/2106.01555/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Detecting cognitive decline using speech only: The ADReSSo Challenge](https://mlsanity.com/paper/2104.09356/) (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)
- [Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity](https://mlsanity.com/paper/2009.00700/) (2020)
- [The INESC-ID Multi-Modal System for the ADReSS 2020 Challenge](https://mlsanity.com/paper/2005.14646/) (2020)
- [Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others](https://mlsanity.com/paper/1904.01684/) (2019)
- [Detecting cognitive impairments by agreeing on interpretations of linguistic features](https://mlsanity.com/paper/1808.06570/) (2018)
- [The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech](https://mlsanity.com/paper/1811.12254/) (2018)
- [Semi-supervised classification by reaching consensus among modalities](https://mlsanity.com/paper/1805.09366/) (2018)
- [Augmenting BERT Carefully with Underrepresented Linguistic Features](https://mlsanity.com/paper/2011.06153/) (2020)

## Built on by (most cited first, 21 of 21)

- [Detecting Dementia from Speech and Transcripts using Transformers](https://mlsanity.com/paper/2110.14769/) (2021)
- [Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection](https://mlsanity.com/paper/2206.13232/) (2022)
- [Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection](https://mlsanity.com/paper/2303.08019/) (2023)
- [Reasoning-Based Approach with Chain-of-Thought for Alzheimer's Detection Using Speech and Large Language Models](https://mlsanity.com/paper/2506.01683/) (2025)
- [Infusing Acoustic Pause Context into Text-Based Dementia Assessment](https://mlsanity.com/paper/2408.15188/) (2024)
- [HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech](https://mlsanity.com/paper/2405.03952/) (2024)
- [Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data](https://mlsanity.com/paper/2210.03303/) (2022)
- [Learning Co-Speech Gesture for Multimodal Aphasia Type Detection](https://mlsanity.com/paper/2310.11710/) (2023)
- [Automatic Prediction of Amyotrophic Lateral Sclerosis Progression using Longitudinal Speech Transformer](https://mlsanity.com/paper/2406.18625/) (2024)
- [Swin-BERT: A Feature Fusion System designed for Speech-based Alzheimer's Dementia Detection](https://mlsanity.com/paper/2410.07277/) (2024)
- [Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry?](https://mlsanity.com/paper/2203.17110/) (2022)
- [Cost-effective Models for Detecting Depression from Speech](https://mlsanity.com/paper/2302.09214/) (2023)
- [Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning](https://mlsanity.com/paper/2505.17067/) (2025)
- [MoTAS: MoE-Guided Feature Selection from TTS-Augmented Speech for Enhanced Multimodal Alzheimer's Early Screening](https://mlsanity.com/paper/2508.20513/) (2025)
- [Bias and Fairness in Self-Supervised Acoustic Representations for Cognitive Impairment Detection](https://mlsanity.com/paper/2603.02937/) (2026)
- [Nonwords Pronunciation Classification in Language Development Tests for Preschool Children](https://mlsanity.com/paper/2206.08058/) (2022)
- [SoftStep: Learning Sparse Similarity Powers Deep Neighbor-Based Regression](https://mlsanity.com/paper/2506.08139/) (2025)
- [Recognizing Dementia from Neuropsychological Tests with State Space Models](https://mlsanity.com/paper/2507.10311/) (2025)
- [Foundation Model-based Evaluation of Neuropsychiatric Disorders: A Lifespan-Inclusive, Multi-Modal, and Multi-Lingual Study](https://mlsanity.com/paper/2512.20948/) (2025)
- [A Multimodal Framework for Dementia Detection via Linguistic and Acoustic Representation Learning](https://mlsanity.com/paper/2605.25540/) (2026)
- [Gated Multi-Graph Fusion via Graph Attention Networks for Alzheimer's Disease Detection](https://mlsanity.com/paper/2606.31186/) (2026)

## Abstract (arXiv)

Robust strategies for Alzheimer's disease (AD) detection are important, given the high prevalence of AD. In this paper, we study the performance and generalizability of three approaches for AD detection from speech on the recent ADReSSo challenge dataset: 1) using conventional acoustic features 2) using novel pre-trained acoustic embeddings 3) combining acoustic features and embeddings. We find that while feature-based approaches have a higher precision, classification approaches relying on pre-trained embeddings prove to have a higher, and more balanced cross-validated performance across multiple metrics of performance. Further, embedding-only approaches are more generalizable. Our best model outperforms the acoustic baseline in the challenge by 2.8%.