# Explainable Identification of Dementia from Transcripts using Transformer Networks

arXiv: https://arxiv.org/abs/2109.06980 · HTML: https://mlsanity.com/paper/2109.06980/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://mlsanity.com/paper/2008.02496/) (2020)
- [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)
- [Detecting Linguistic Characteristics of Alzheimer's Dementia by Interpreting Neural Models](https://mlsanity.com/paper/1804.06440/) (2018)
- [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)
- [Joint Modelling of Emotion and Abusive Language Detection](https://mlsanity.com/paper/2005.14028/) (2020)
- [Modeling the Severity of Complaints in Social Media](https://mlsanity.com/paper/2103.12428/) (2021)
- [Enriching Neural Models with Targeted Features for Dementia Detection](https://mlsanity.com/paper/1906.05483/) (2019)

## Built on by (most cited first, 19 of 19)

- [Detecting Dementia from Speech and Transcripts using Transformers](https://mlsanity.com/paper/2110.14769/) (2021)
- [Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media](https://mlsanity.com/paper/2305.16797/) (2023)
- [LLMCARE: early detection of cognitive impairment via transformer models enhanced by LLM-generated synthetic data](https://mlsanity.com/paper/2508.10027/) (2025)
- [CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection](https://mlsanity.com/paper/2506.01890/) (2025)
- [Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech](https://mlsanity.com/paper/2305.16406/) (2023)
- [Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia](https://mlsanity.com/paper/2302.05894/) (2023)
- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [Improving Transformer Performance for French Clinical Notes Classification Using Mixture of Experts on a Limited Dataset](https://mlsanity.com/paper/2303.12892/) (2023)
- [Multitask learning for recognizing stress and depression in social media](https://mlsanity.com/paper/2305.18907/) (2023)
- [Exploiting Longitudinal Speech Sessions via Voice Assistant Systems for Early Detection of Cognitive Decline](https://mlsanity.com/paper/2410.12885/) (2024)
- [Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection](https://mlsanity.com/paper/2409.12541/) (2024)
- [Depression detection in social media posts using transformer-based models and auxiliary features](https://mlsanity.com/paper/2409.20048/) (2024)
- [Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness](https://mlsanity.com/paper/2505.13418/) (2025)
- [Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques](https://mlsanity.com/paper/2410.18972/) (2024)
- [Understanding Dementia Speech Alignment with Diffusion-Based Image Generation](https://mlsanity.com/paper/2508.09385/) (2025)
- [Multilingual Cognitive Impairment Detection in the Era of Foundation Models](https://mlsanity.com/paper/2604.06758/) (2026)
- [Task-guided Spatiotemporal Network with Diffusion Augmentation for EEG-based Dementia Diagnosis and MMSE Prediction](https://mlsanity.com/paper/2604.23964/) (2026)
- [A Multimodal Framework for Dementia Detection via Linguistic and Acoustic Representation Learning](https://mlsanity.com/paper/2605.25540/) (2026)
- [From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection](https://mlsanity.com/paper/2606.27973/) (2026)

## Abstract (arXiv)

Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time. Very few works have exploited transformer-based networks and despite the high accuracy achieved, little work has been done in terms of model interpretability. In addition, although Mini-Mental State Exam (MMSE) scores are inextricably linked with the identification of dementia, research works face the task of dementia identification and the task of the prediction of MMSE scores as two separate tasks. In order to address these limitations, we employ several transformer-based models, with BERT achieving the highest accuracy accounting for 87.50%. Concurrently, we propose an interpretable method to detect AD patients based on siamese networks reaching accuracy up to 83.75%. Next, we introduce two multi-task learning models, where the main task refers to the identification of dementia (binary classification), while the auxiliary one corresponds to the identification of the severity of dementia (multiclass classification). Our model obtains accuracy equal to 86.25% on the detection of AD patients in the multi-task learning setting. Finally, we present some new methods to identify the linguistic patterns used by AD patients and non-AD ones, including text statistics, vocabulary uniqueness, word usage, correlations via a detailed linguistic analysis, and explainability techniques (LIME). Findings indicate significant differences in language between AD and non-AD patients.