# Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge

arXiv: https://arxiv.org/abs/2301.05562 · HTML: https://mlsanity.com/paper/2301.05562/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review](https://mlsanity.com/paper/2010.06047/) (2020)

## Built on by (most cited first, 18 of 18)

- [Connected Speech-Based Cognitive Assessment in Chinese and English](https://mlsanity.com/paper/2406.10272/) (2024)
- [Cross-lingual Alzheimer's Disease detection based on paralinguistic and pre-trained features](https://mlsanity.com/paper/2303.07650/) (2023)
- [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)
- [HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech](https://mlsanity.com/paper/2405.03952/) (2024)
- [False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK](https://mlsanity.com/paper/2602.13047/) (2026)
- [PROCESS-2: A Benchmark Speech Corpus for Early Cognitive Impairment Detection](https://mlsanity.com/paper/2605.14888/) (2026)
- [Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review](https://mlsanity.com/paper/2307.09230/) (2023)
- [Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques](https://mlsanity.com/paper/2410.18972/) (2024)
- [MultiConAD: A Unified Multilingual Conversational Dataset for Early Alzheimer's Detection](https://mlsanity.com/paper/2502.19208/) (2025)
- [Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning](https://mlsanity.com/paper/2505.17067/) (2025)
- [The order in speech disorder: a scoping review of state of the art machine learning methods for clinical speech classification](https://mlsanity.com/paper/2503.04802/) (2025)
- [Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands](https://mlsanity.com/paper/2505.17137/) (2025)
- [Foundation Model-based Evaluation of Neuropsychiatric Disorders: A Lifespan-Inclusive, Multi-Modal, and Multi-Lingual Study](https://mlsanity.com/paper/2512.20948/) (2025)
- [SpeechDx: A Multi-Task Benchmark for Clinical Speech AI](https://mlsanity.com/paper/2606.17339/) (2026)
- [Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews](https://mlsanity.com/paper/2606.18019/) (2026)
- [Mitigating Scoring Errors and Compensating for Nonverbal Subtests in Speech-Based Dementia Assessment](https://mlsanity.com/paper/2606.18979/) (2026)
- [Cleaner Speech, Weaker Generalization: Revisiting Pitt-Derived Benchmarks for Alzheimer's Disease Detection](https://mlsanity.com/paper/2609.00276/) (2026)
- [A Cross-Lingual Acoustic Disease-Alignment Framework for Respiratory Health Assessment from Spontaneous Speech](https://mlsanity.com/paper/2609.19398/) (2026)

## Abstract (arXiv)

This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD). Participants were invited to employ signal processing and machine learning methods to create predictive models based on spontaneous speech data. The Challenge has been designed to assess the extent to which predictive models built based on speech in one language (English) generalise to another language (Greek). To the best of our knowledge no work has investigated acoustic features of the speech signal in multilingual AD detection. Our baseline system used conventional machine learning algorithms with Active Data Representation of acoustic features, achieving accuracy of 73.91% on AD detection, and 4.95 root mean squared error on cognitive score prediction.