# Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speech

arXiv: https://arxiv.org/abs/2006.07358 · HTML: https://mlsanity.com/paper/2006.07358/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection](https://mlsanity.com/paper/2206.13232/) (2022)
- [Exploring linguistic feature and model combination for speech recognition based automatic AD detection](https://mlsanity.com/paper/2206.13758/) (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)
- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (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)
- [The Unreliability of Acoustic Systems in Alzheimer's Speech Datasets with Heterogeneous Recording Conditions](https://mlsanity.com/paper/2409.12170/) (2024)
- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [NeuraHealth: An Automated Screening Pipeline to Detect Undiagnosed Cognitive Impairment in Electronic Health Records with Deep Learning and Natural Language Processing](https://mlsanity.com/paper/2202.00478/) (2022)
- [Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's](https://mlsanity.com/paper/2209.06085/) (2022)
- [AI and Non AI Assessments for Dementia](https://mlsanity.com/paper/2307.01210/) (2023)
- [A Multimodal Framework for Dementia Detection via Linguistic and Acoustic Representation Learning](https://mlsanity.com/paper/2605.25540/) (2026)
- [LSEAD: A Privacy-Preserving LLM-Based Speech Analysis Framework for Early Alzheimer's Disease Screening](https://mlsanity.com/paper/2608.07378/) (2026)

## Abstract (arXiv)

Alzheimer's Dementia (AD) is an incurable, debilitating, and progressive neurodegenerative condition that affects cognitive function. Early diagnosis is important as therapeutics can delay progression and give those diagnosed vital time. Developing models that analyse spontaneous speech could eventually provide an efficient diagnostic modality for earlier diagnosis of AD. The Alzheimer's Dementia Recognition through Spontaneous Speech task offers acoustically pre-processed and balanced datasets for the classification and prediction of AD and associated phenotypes through the modelling of spontaneous speech. We exclusively analyse the supplied textual transcripts of the spontaneous speech dataset, building and comparing performance across numerous models for the classification of AD vs controls and the prediction of Mental Mini State Exam scores. We rigorously train and evaluate Support Vector Machines (SVMs), Gradient Boosting Decision Trees (GBDT), and Conditional Random Fields (CRFs) alongside deep learning Transformer based models. We find our top performing models to be a simple Term Frequency-Inverse Document Frequency (TF-IDF) vectoriser as input into a SVM model and a pre-trained Transformer based model `DistilBERT' when used as an embedding layer into simple linear models. We demonstrate test set scores of 0.81-0.82 across classification metrics and a RMSE of 4.58.