# To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection

arXiv: https://arxiv.org/abs/2008.01551 · HTML: https://mlsanity.com/paper/2008.01551/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2020.

## Built on by (most cited first, 30 of 49)

- [DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature](https://mlsanity.com/paper/2405.04819/) (2024)
- [Comparing Acoustic-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2106.01555/) (2021)
- [Explainable Identification of Dementia from Transcripts using Transformer Networks](https://mlsanity.com/paper/2109.06980/) (2021)
- [Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models](https://mlsanity.com/paper/2106.08689/) (2021)
- [Detecting Dementia from Speech and Transcripts using Transformers](https://mlsanity.com/paper/2110.14769/) (2021)
- [Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection](https://mlsanity.com/paper/2303.08019/) (2023)
- [Exploring linguistic feature and model combination for speech recognition based automatic AD detection](https://mlsanity.com/paper/2206.13758/) (2022)
- [Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?](https://mlsanity.com/paper/2401.05551/) (2024)
- [GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models](https://mlsanity.com/paper/2203.13397/) (2022)
- [CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speech](https://mlsanity.com/paper/2407.13660/) (2024)
- [Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease Detection based on Spontaneous Speech](https://mlsanity.com/paper/2412.15772/) (2024)
- [LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data](https://mlsanity.com/paper/2508.10027/) (2025)
- [Data Augmentation for Dementia Detection in Spoken Language](https://mlsanity.com/paper/2206.12879/) (2022)
- [Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection](https://mlsanity.com/paper/2210.16539/) (2022)
- [Robustness and Sensitivity of BERT Models Predicting Alzheimer's Disease from Text](https://mlsanity.com/paper/2109.11888/) (2021)
- [Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses](https://mlsanity.com/paper/2306.03443/) (2023)
- [Infusing Acoustic Pause Context into Text-Based Dementia Assessment](https://mlsanity.com/paper/2408.15188/) (2024)
- [Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings](https://mlsanity.com/paper/2104.10715/) (2021)
- [Alzheimers Dementia Detection using Acoustic & Linguistic features and Pre-Trained BERT](https://mlsanity.com/paper/2109.11010/) (2021)
- [TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments](https://mlsanity.com/paper/2302.07322/) (2023)
- [Evaluating Picture Description Speech for Dementia Detection using Image-text Alignment](https://mlsanity.com/paper/2308.07933/) (2023)
- [Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia](https://mlsanity.com/paper/2310.09897/) (2023)
- [Cognitive Insights Across Languages: Enhancing Multimodal Interview Analysis](https://mlsanity.com/paper/2406.07542/) (2024)
- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning](https://mlsanity.com/paper/2111.07454/) (2021)
- [The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification](https://mlsanity.com/paper/2211.07430/) (2022)
- [Learning Co-Speech Gesture for Multimodal Aphasia Type Detection](https://mlsanity.com/paper/2310.11710/) (2023)
- [Confidence Estimation for Automatic Detection of Depression and Alzheimer's Disease Based on Clinical Interviews](https://mlsanity.com/paper/2407.19984/) (2024)
- [Zero-Shot Cognitive Impairment Detection from Speech Using AudioLLM](https://mlsanity.com/paper/2506.17351/) (2025)
- [Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry?](https://mlsanity.com/paper/2203.17110/) (2022)

## Abstract (arXiv)

Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset: 1) using domain knowledge-based hand-crafted features that capture linguistic and acoustic phenomena, and 2) fine-tuning Bidirectional Encoder Representations from Transformer (BERT)-based sequence classification models. We also compare multiple feature-based regression models for a neuropsychological score task in the challenge. We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task.