# Detecting Alzheimer's Disease Using Gated Convolutional Neural Network from Audio Data

arXiv: https://arxiv.org/abs/1803.11344 · HTML: https://mlsanity.com/paper/1803.11344/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing](https://mlsanity.com/paper/2302.14638/) (2023)
- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [On the Impact of Voice Anonymization on Speech Diagnostic Applications: a Case Study on COVID-19 Detection](https://mlsanity.com/paper/2304.02181/) (2023)
- [Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening](https://mlsanity.com/paper/2606.14788/) (2026)
- [Passive Dementia Screening via Facial Temporal Micro-Dynamics Analysis of In-the-Wild Talking-Head Video](https://mlsanity.com/paper/2511.13802/) (2025)

## Abstract (arXiv)

We propose an automatic detection method of Alzheimer's diseases using a gated convolutional neural network (GCNN) from speech data. This GCNN can be trained with a relatively small amount of data and can capture the temporal information in audio paralinguistic features. Since it does not utilize any linguistic features, it can be easily applied to any languages. We evaluated our method using Pitt Corpus. The proposed method achieved the accuracy of 73.6%, which is better than the conventional sequential minimal optimization (SMO) by 7.6 points.