PROCESS-2: A Benchmark Speech Corpus for Early Cognitive Impairment Detection
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866918501711609856 |
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| author | Pahar, Madhurananda Illingworth, Caitlin H. Mirheidari, Bahman Elghazaly, Hend Peters, Fritz Young, Sophie Leung, Wing-Zin Kaur, Labhpreet Blackburn, Daniel Christensen, Heidi |
| author_facet | Pahar, Madhurananda Illingworth, Caitlin H. Mirheidari, Bahman Elghazaly, Hend Peters, Fritz Young, Sophie Leung, Wing-Zin Kaur, Labhpreet Blackburn, Daniel Christensen, Heidi |
| contents | Speech-based analysis offers a scalable and non-invasive approach for detecting cognitive decline, yet progress has been constrained by the limited availability of clinically validated datasets collected under realistic conditions. We introduce PROCESS-2, a large-scale speech dataset designed to support research on automatic assessment of cognitive impairment from spontaneous and task-oriented speech. The dataset comprises recordings from 200 healthy controls, 150 mild cognitive impairment, and 50 dementia diagnoses collected using the CognoMemory digital assessment platform. Each participant completed a single assessment session, including picture description and verbal fluency tasks, accompanied by manually verified transcripts and participant-level metadata. PROCESS-2 contains approximately 21 hours of speech audio with predefined train/test partitions. Comprehensive technical validation evaluated demographic balance, clinical consistency, recording stability, embedding-space structure, and reproducible baseline modelling performance, demonstrating clinically meaningful group separation and stable performance across modelling approaches while preserving real-world conversational variability. PROCESS-2 is released under controlled access via Hugging Face to enable responsible reuse while protecting participant privacy, providing a reproducible benchmark resource for speech-based cognitive assessment research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14888 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PROCESS-2: A Benchmark Speech Corpus for Early Cognitive Impairment Detection Pahar, Madhurananda Illingworth, Caitlin H. Mirheidari, Bahman Elghazaly, Hend Peters, Fritz Young, Sophie Leung, Wing-Zin Kaur, Labhpreet Blackburn, Daniel Christensen, Heidi Sound Machine Learning Speech-based analysis offers a scalable and non-invasive approach for detecting cognitive decline, yet progress has been constrained by the limited availability of clinically validated datasets collected under realistic conditions. We introduce PROCESS-2, a large-scale speech dataset designed to support research on automatic assessment of cognitive impairment from spontaneous and task-oriented speech. The dataset comprises recordings from 200 healthy controls, 150 mild cognitive impairment, and 50 dementia diagnoses collected using the CognoMemory digital assessment platform. Each participant completed a single assessment session, including picture description and verbal fluency tasks, accompanied by manually verified transcripts and participant-level metadata. PROCESS-2 contains approximately 21 hours of speech audio with predefined train/test partitions. Comprehensive technical validation evaluated demographic balance, clinical consistency, recording stability, embedding-space structure, and reproducible baseline modelling performance, demonstrating clinically meaningful group separation and stable performance across modelling approaches while preserving real-world conversational variability. PROCESS-2 is released under controlled access via Hugging Face to enable responsible reuse while protecting participant privacy, providing a reproducible benchmark resource for speech-based cognitive assessment research. |
| title | PROCESS-2: A Benchmark Speech Corpus for Early Cognitive Impairment Detection |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2605.14888 |