# Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity

arXiv: https://arxiv.org/abs/2009.00700 · HTML: https://mlsanity.com/paper/2009.00700/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [Comparing Acoustic-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2106.01555/) (2021)
- [Detecting Dementia from Speech and Transcripts using Transformers](https://mlsanity.com/paper/2110.14769/) (2021)
- [Exploring linguistic feature and model combination for speech recognition based automatic AD detection](https://mlsanity.com/paper/2206.13758/) (2022)
- [Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection](https://mlsanity.com/paper/2210.16539/) (2022)
- [A Multimodal Approach for Dementia Detection from Spontaneous Speech with Tensor Fusion Layer](https://mlsanity.com/paper/2211.04368/) (2022)
- [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)
- [Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses](https://mlsanity.com/paper/2306.03443/) (2023)
- [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)
- [Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia](https://mlsanity.com/paper/2302.05894/) (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)
- [Efficient Multimodal Neural Networks for Trigger-less Voice Assistants](https://mlsanity.com/paper/2305.12063/) (2023)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Streaming Anchor Loss: Augmenting Supervision with Temporal Significance](https://mlsanity.com/paper/2310.05886/) (2023)
- [A Robust Classification Method using Hybrid Word Embedding for Early Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2510.14332/) (2025)

## Abstract (arXiv)

Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia