The DigiDiaDem Speech-Cognitive Dataset: Initial Experiments on Detecting Cognitive Impairments from Speech

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F26%3A43929447" target="_blank" >RIV/00064173:_____/26:43929447 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ACCESS.2026.3662045" target="_blank" >https://doi.org/10.1109/ACCESS.2026.3662045</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2026.3662045" target="_blank" >10.1109/ACCESS.2026.3662045</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The DigiDiaDem Speech-Cognitive Dataset: Initial Experiments on Detecting Cognitive Impairments from Speech

Popis výsledku v původním jazyce

Dementia is a growing global health challenge, making early detection of cognitive decline critically important. Early detection of dementia and other cognitive impairment is essential for timely intervention and better care planning. However, existing datasets for training automated screening tools are limited, especially for underrepresented languages such as Czech. In this study, we present a new dataset and a novel application named the DigiDiaDem (Digital Diagnostics of Dementia) designed for automated dementia screening through multimodal cognitive assessment. The application integrates user-friendly digital cognitive tasks with machine learning algorithms to evaluate linguistic and cognitive performance in real time. Using this system, we collected and curated a comprehensive dataset of speech and cognitive data from Czech-speaking participants. The dataset comprises 371 individuals, including both cognitively normal individuals and patients with mild cognitive impairment and mild dementia. It includes socio-demographic data, cognitive and speech tests, functional assessment questionnaires, data collected through the DigiDiaDem application, and text from recognized speech recordings with manually engineered linguistic and acoustic features. We describe the data collection process and outline the cognitive tasks used to collect the dataset. Our experiments demonstrate that speech features derived from cognitively demanding tasks, such as verbal fluency and memory recall, can effectively distinguish healthy participants from those with cognitive impairment. Models trained on the dataset achieved up to 95% accuracy when combining speech features with demographic information. Preliminary experiments demonstrate the feasibility of using the collected data for dementia detection. These findings confirm that speech-based digital assessment can complement traditional clinical evaluation. The proposed dataset and application offer a substantial resource for the research community by establishing a solid baseline for machine-learning-based approaches to dementia screening from speech-based interaction.

Název v anglickém jazyce

The DigiDiaDem Speech-Cognitive Dataset: Initial Experiments on Detecting Cognitive Impairments from Speech

Popis výsledku anglicky

Dementia is a growing global health challenge, making early detection of cognitive decline critically important. Early detection of dementia and other cognitive impairment is essential for timely intervention and better care planning. However, existing datasets for training automated screening tools are limited, especially for underrepresented languages such as Czech. In this study, we present a new dataset and a novel application named the DigiDiaDem (Digital Diagnostics of Dementia) designed for automated dementia screening through multimodal cognitive assessment. The application integrates user-friendly digital cognitive tasks with machine learning algorithms to evaluate linguistic and cognitive performance in real time. Using this system, we collected and curated a comprehensive dataset of speech and cognitive data from Czech-speaking participants. The dataset comprises 371 individuals, including both cognitively normal individuals and patients with mild cognitive impairment and mild dementia. It includes socio-demographic data, cognitive and speech tests, functional assessment questionnaires, data collected through the DigiDiaDem application, and text from recognized speech recordings with manually engineered linguistic and acoustic features. We describe the data collection process and outline the cognitive tasks used to collect the dataset. Our experiments demonstrate that speech features derived from cognitively demanding tasks, such as verbal fluency and memory recall, can effectively distinguish healthy participants from those with cognitive impairment. Models trained on the dataset achieved up to 95% accuracy when combining speech features with demographic information. Preliminary experiments demonstrate the feasibility of using the collected data for dementia detection. These findings confirm that speech-based digital assessment can complement traditional clinical evaluation. The proposed dataset and application offer a substantial resource for the research community by establishing a solid baseline for machine-learning-based approaches to dementia screening from speech-based interaction.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30210 - Clinical neurology

Projekt

<a href="/cs/project/TQ01000332" target="_blank" >TQ01000332: Telemedicínské samovyšetření řeči a paměti pro rychlou detekci kognitivních poruch metodami strojového učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2026

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

14

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

22232-22251

Kód UT WoS článku

001691066800009

EID výsledku v databázi Scopus

2-s2.0-105029638603