The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Alzheimer’s Dementia Recognition Through Spontaneous Speech: The ADReSS Challenge
Open accessAbstract
The ADReSS Challenge at INTERSPEECH 2020 defines a shared task through which different approaches to the automated recognition of Alzheimer's dementia based on spontaneous speech can be compared. ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task. In the Alzheimer's speech classification task, ADReSS challenge participants create models for classifying speech as dementia or healthy control speech. In the the neuropsychological score regression task, participants create models to predict mini-mental state examination scores. This paper describes the ADReSS Challenge in detail and presents a baseline for both tasks, including feature extraction procedures and results for classification and regression models. ADReSS aims to provide the speech and language Alzheimer's research community with a platform for comprehensive methodological comparisons. This will hopefully contribute to addressing the lack of standardisation that currently affects the field and shed light on avenues for future research and clinical applicability.
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Citations
Cites
Opensmile: the munich versatile and fast open-source audio feature extractor
Eyben, F., Wöllmer, M., & Schuller, B. (2010) · Proceedings of the 18th ACM international conference on Multimedia · doi:10.1145/1873951.1874246 · Cited by 2,642
The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., Andre, E., Busso, C., Devillers, L. Y., Epps, J., Laukka, P., Narayanan, S. S., et al. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2457417 · Cited by 1,818
Recent developments in openSMILE, the munich open-source multimedia feature extractor
Eyben, F., Weninger, F. J., Gross, F., & Schuller, B. W. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502224 · Cited by 1,265
Linguistic Features Identify Alzheimer’s Disease in Narrative Speech
Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-150520 · Cited by 797
On Ideal Binary Mask As the Computational Goal of Auditory Scene Analysis
Wang, D. (2006) · Kluwer Academic Publishers eBooks · doi:10.1007/0-387-22794-6_12 · Cited by 566
Haider, F., García, S. D. L. F., & Luz, S. F. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2955022 · Cited by 204
Self-citationA Feature Study for Classification-Based Speech Separation at Low Signal-to-Noise Ratios
Chen, J., Wang, Y., & Wang, D. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2359159 · Cited by 159
Hernández-Domínguez, L., Ratté, S., Sierra, G., & Roche-Bergua, A. (2018) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.02.004 · Cited by 124
Delirium, dementia, and amnestic and other cognitive disorders
Tucker, G. J. (1984) · Medical Entomology and Zoology · Cited by 104
Voice Activity Detection Using an Adaptive Context Attention Model
Kim, J., & Hahn, M. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2811740 · Cited by 103
Flagged first, then by citations
Research works printed but not linked: 5
- World Health Organization, "Mental health action plan 2013- 2020," WHO Library Cataloguing-in-Publication DataLibrary Cataloguing-in-Publication Data, pp. 1-44, 2013.
- H. Goodglass, E. Kaplan, and B. Barresi, BDAE-3: Boston Di- agnostic Aphasia Examination -Third Edition. Lippincott
- --, "The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing," vol. 7, no. 2, pp. 190- 202, 2016.
- M. Yancheva and F. Rudzicz, "Vector-space topic models for de- tecting Alzheimers disease," in Procs. of ACL, 2016, pp. 2337- 2346.
- M. Yancheva, K. Fraser, and F. Rudzicz, "Using linguistic fea- tures longitudinally to predict clinical scores for Alzheimers dis- ease and related dementias," in Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Tech- nologies, 2015, pp. 134-139.
Cited by
Sorted by citations
Items 201–274 of 274
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Meza, M., Gauder, L., Estienne, L., Barchi, R. G., Gravano, A., Riera, P., & Ferrer, L. (2023) · Workshop on Speech, Music and Mind (SMM) · doi:10.21437/smm.2023-1 · Cited by 1
Speaker-turn aware diarization for speech-based cognitive assessments
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Dong, Z., Guo, H., Xu, W., Zhao, H., & Zhang, Z. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3622051 · Cited by 2
Analytical Review of Speech and Multimodal Methods for Cognitive Impairments Recognition in People
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Aung, N. N., & Pratanwanich, N. (2025) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse67377.2025.11297915 · Cited by 1
Li, R., Berlowitz, D. R., Mez, J., Silver, B., Wang, X., Hu, W., Goodwin, R., Keating, H., Liu, W., Lin, H., et al. (2026) · Communications Medicine · doi:10.1038/s43856-025-01206-w · Cited by 1
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Dementia classification using attention mechanism on audio data
Milana, S. (2023) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami58000.2023.10044539 · Cited by 1
A Digital Language Coherence Marker for Monitoring Dementia
Gkoumas, D., Tsakalidis, A., & Liakata, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.994 · Cited by 2
VoiceAD: A multilingual speech based classifier for early detection of Alzheimer’s disease
Wu, A. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/86/20241520 · Cited by 1
Kang, J., Li, J., Li, J., Wu, X., & Meng, H. M. L. (2024) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp63861.2024.10800483 · Cited by 2
Leveraging Multimodal Vision Language Models for Early Detection of Alzheimer’s Disease
Casu, F., Grosso, E., Lagorio, A., Ruiu, P., & Trunfio, G. A. (2025) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp66500.2025.00047 · Cited by 1
Dabbabi, K., Kehilib, A., & Cherif, A. (2025) · MOJ Applied Bionics and Biomechanics · doi:10.15406/mojabb.2025.09.00221 · Cited by 1
(2022) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2022.06.041 · Cited by 1
Acoustic and Linguistic Analyses to Predict Cognitive Decline Progression in Alzheimer’s Disease
(2022) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2022.06.024 · Cited by 1
Speech Features for Monitoring Alzheimer’s Disease Using Random Forest Classifier
Hason, L. (2024) · doi:10.32920/25412647 · Cited by 1
Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's DementiaPreprint
Sarawgi, U., Zulfikar, W. D., Khincha, R., & Maes, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01440
Leveraging Clinical Notes and Natural Language Processing for Dementia Detection (Preprint)Preprint
Liu, M., Beare, R. J., & Andrew, N. E. (2022) · doi:10.2196/preprints.43417
Slikker, W., Wu, L., Xu, J., & Shafer, T. J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824010-6.00048-4
Cheng, S., Du, J., Niu, S., Cristià, A., Wang, X., Wang, Q., & Lee, C. (2023) · Speech Communication · doi:10.1016/j.specom.2023.102956
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Lin, K., & Washington, P. Y. (2023) · Research Square · doi:10.21203/rs.3.rs-3550344/v1
Ke, X., Mak, M., & Meng, H. M. L. (2023) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc58517.2023.10317278
Machine Learning, Features, and Computational Approaches to Discourse Analysis
Clarke, N., & Garrard, P. (2023) · Springer eBooks · doi:10.1007/978-3-031-45190-4_17
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Hsu, W.-T., Chen, C.-P., & Lee, C.-C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446570
Lin, N., Zhu, Y., Liang, X., Batsis, J. A., & Summerour, C. E. (2024) · Interspeech (USB) · doi:10.21437/interspeech.2024-2288
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Agaskar, V. A., Vishwakarma, R., Rawat, M., & Achrekar, O. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-69197-3_1
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Schiavon, D. E. B., & Becker, C. D. L. (2024) · Journal of Health Informatics · doi:10.59681/2175-4411.v16.iespecial.2024.1254
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Sartori, R., Marinaro, F., Tommasi, F., Buccoliero, A., Zene, M., Shah, S. A. H., & Ceschi, A. (2025) · European Journal of Psychological Assessment · doi:10.1027/1015-5759/a000915
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Deniz, E., Hancı, N. B., Erdem, O., & Seren, B. K. (2025) · Signal Processing: Algorithms, Architectures, Arrangements, and Applications · doi:10.23919/spa65537.2025.11215093
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Farmanbar, M., Freja, S. A., & Shakeri, A. (2025) · Frontiers of artificial intelligence, ethics and multidisciplinary applications. · doi:10.1007/978-981-96-7945-4_11
Mallol-Ragolta, A., Gonzalez-Machorro, M., von Heynitz, R. K., Scherzer, K. Y., Cordts, I., & Schuller, B. W. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254090
Sharan, R. V., Xiong, H., & Tseng, Y. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254116
Mehmood, A., Shahid, F., Khan, R., & AlZu’bi, H. S. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.12.032
Liu, Y.-L., Li, Y., Feng, R., Chen, J., Wang, Y., Chen, Y., Ding, N., Yuan, J., & Ling, Z.-H. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104968
Sharma, S., Kulkarni, K., & Gonuguntla, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8309-3_21
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Su, P., Miao, Y., Guo, C., Tang, J., Li, S., & Wang, T. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356214
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Datta, H., Sawarkar, S., Tijare, A. V., Banait, D., & Gotmare, V. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362426
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Shakeri, A., & Farmanbar, M. (2026) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70246
Chen, Z., Lu, M., Ding, Z., Guan, T., Lai, Z., Li, Z., Huang, J., Huang, G., Lyu, Y., & Liu, Y. (2026) · Health · doi:10.4236/health.2026.183015
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Lv, T., & Hou, M. (2025) · 2025 5th International Symposium on Artificial Intelligence and Big Data (AIBDF) · doi:10.1109/aibdf67964.2025.11440865
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Riad, R. (2022) · theses.fr (ABES) · doi:10.70675/1044c136z4a18z4b78zb00dz314b3b823381 · Cited by 1
Interpretable Alzheimer’s Disease Detection Via Multi-Scale Fusion of Disentangled Speech Features
Chen, Y., Liu, X., Su, R., Wang, L., & Yan, N. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460895
Liu, Y.-L., Li, Y., Chen, Y., He, L., Feng, R., Chen, J., & Yuan, J. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462438
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Rajaprakash, S., Murugaraj, S. S., Boopalan, K., Kumar, D. D., Harish, M., & Chandru, M. S. (2026) · IEEE International Conference for Convergence in Computing Technology (I3CTCON) · doi:10.1109/i3ctcon68242.2026.11507593
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Ramos-Sanchez, G., Ortiz-Pérez, D., Ψαρρού, Α., & Garcia-Rodriguez, J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29254-4_50
Braun, F., Rüggeberg, A., Ranzenberger, T., Lehfeld, H., Hillemacher, T., Bocklet, T., & Riedhammer, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37249-9_49
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Lee, M., Meylan, S. C., Shobin, E., Nisenbaum, L., Sukumar, A., & Kourtis, L. C. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71489
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Gao, Y., Wu, Y., Yang, F., Zhou, Y., Li, Y., & Wu, M. (2026) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-026-2943-8
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Seghroucheni, Y. Z., Hanae, B., & Minaoui, K. (2026) · International Symposium on Signal, Image, Video and Communications (ISIVC) · doi:10.1109/isivc69944.2026.11574163
Mahmoud, M. A., Mahmoud, T. A., & Elhadad, M. K. (2026) · ICEENG International Conference for Innovations in Intelligent Computing and Cybersecurity (IICC) · doi:10.1109/iicc69623.2026.11582631
Lech, M., Syed, M. S. S., & Syed, Z. S. (2026) · Springer eBooks · doi:10.1007/978-3-032-11023-7_8
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Ramineni, V., Khan, F. F., Kim, J.-H., & Kwon, G. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1975870
Carney, J. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261498421
Metadata
- Authors
- Luz, S., Haider, F., Fuente, S. D. L., Fromm, D., & MacWhinney, B.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Interspeech 2020
- Publisher
- ISCA
- Published
- 2020-10-25
- Pages
- 2172-2176
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W3016396388
- Cited by
- 274
- References
- 19
- Citations by year
- 2026: 29 · 2025: 45 · 2024: 46 · 2023: 45 · 2022: 41 · 2021: 42 · 2020: 26
- FWCI
- 60.28
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission (HORIZON2020, 769661)
- Medical Research Council
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- Alzheimer's disease, dementia detection, spontaneous speech, speech classification, Mini-Mental State Examination, benchmark dataset, shared task, cognitive assessment
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