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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
García, S. D. L. F., Ritchie, C. W., & Luz, S. F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06047 · Cited by 265
Self-citationDetecting Cognitive Decline Using Speech Only: The ADReSSo Challenge
Luz, S. F., Haider, F., de la Fuente, S., Fromm, D., & MacWhinney, B. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1220 · Cited by 163
Self-citationMeghanani, A., Anoop, C. S., & Ramakrishnan, A. G. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383491 · Cited by 134
Balagopalan, A., Eyre, B., Rudzicz, F., & Novikova, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2557 · Cited by 166
DementiaBank: Theoretical Rationale, Protocol, and Illustrative Analyses
Lanzi, A. M., Saylor, A. K., Fromm, D., Liu, H., MacWhinney, B., & Cohen, M. L. (2023) · American Journal of Speech-Language Pathology · doi:10.1044/2022_ajslp-22-00281 · Cited by 117
Self-citationEditorial: Alzheimer's Dementia Recognition through Spontaneous Speech
Luz, S., Haider, F., de la Fuente Garcia, S., Fromm, D., & MacWhinney, B. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.780169 · Cited by 104
Self-citationClassifying Alzheimer's Disease Using Audio and Text-Based Representations of Speech
Haulcy, R., & Glass, J. R. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.624137 · Cited by 110
Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech
Balagopalan, A., Eyre, B., Robin, J., Rudzicz, F., & Novikova, J. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.635945 · Cited by 106
Mahajan, P., & Baths, V. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.623607 · Cited by 89
Rohanian, M., Hough, J., & Purver, M. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2721 · Cited by 97
Speech based detection of Alzheimer's disease: a survey of AI techniques, datasets and challenges
Ding, K., Chetty, M., Noori Hoshyar, A., Bhattacharya, T., & Klein, B. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10961-6 · Cited by 82
Automated Screening for Alzheimer’s Dementia Through Spontaneous Speech
Syed, M. S. S., Syed, Z. S., Lech, M., & Pirogova, E. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-3158 · Cited by 71
Multimodal Inductive Transfer Learning for Detection of Alzheimer’s Dementia and its Severity
Sarawgi, U., Zulfikar, W. D., Soliman, N., & Maes, P. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-3137 · Cited by 89
Exploiting Multi-Modal Features from Pre-Trained Networks for Alzheimer’s Dementia Recognition
Koo, J., Lee, J. H., Pyo, J., Jo, Y., & Lee, K. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-3153 · Cited by 70
Pappagari, R. R., Cho, J., Moro-Velázquez, L., & Dehak, N. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2587 · Cited by 68
Multimodal Deep Learning Models for Detecting Dementia From Speech and Transcripts
Ilias, L., & Askounis, D. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.830943 · Cited by 72
Liu, N., Luo, K., Yuan, Z., & Chen, Y. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.772592 · Cited by 66
Explainable Identification of Dementia From Transcripts Using Transformer Networks
Ilias, L., & Askounis, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172479 · Cited by 70
Semantic Feature Extraction Using SBERT for Dementia Detection
Santander-Cruz, Y., Salazar-Colores, S., Paredes-García, W. J., Guendulain-Arenas, H., & Tovar‐Arriaga, S. (2022) · Brain Sciences · doi:10.3390/brainsci12020270 · Cited by 73
Lindsay, H., Tröger, J., & König, A. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.642033 · Cited by 64
Pappagari, R. R., Cho, J., Joshi, S., Moro-Velázquez, L., Żelasko, P., Villalba, J., & Dehak, N. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1850 · Cited by 67
A Comparison of Acoustic and Linguistics Methodologies for Alzheimer’s Dementia Recognition
Cummins, N., Pan, Y., Ren, Z. X., Fritsch, J., Nallanthighal, V. S., Christensen, H., Blackburn, D. J., Schuller, B. W., Magimai.-Doss, M., Strik, H., et al. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2635 · Cited by 65
A survey on federated learning for security and privacy in healthcare applications
Coelho, K. K., Nogueira, M., Vieira, A. B., Silva, E. F., & Nacif, J. A. M. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.05.012 · Cited by 70
Exploring Deep Transfer Learning Techniques for Alzheimer's Dementia Detection
Zhu, Y., Liang, X., Batsis, J. A., & Roth, R. M. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.624683 · Cited by 61
Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech
Shah, Z., Sawalha, J., Tasnim, M., Qi, S.-A., Stroulia, E., & Greiner, R. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.624659 · Cited by 56
Martinc, M., Haider, F., Pollak, S., & Luz, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.642647 · Cited by 55
Self-citationSearle, T., Ibrahim, Z. M., & Dobson, R. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2729 · Cited by 62
Martinc, M., & Pollak, S. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2202 · Cited by 52
Detecting dementia from speech and transcripts using transformers
Ilias, L., Askounis, D., & Psarras, J. E. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101485 · Cited by 55
Detecting cognitive decline using speech only: The ADReSS O ChallengePreprint
Luz, S. F., Haider, F., García, S. D. L. F., Fromm, D., & MacWhinney, B. (2021) · medRxiv · doi:10.1101/2021.03.24.21254263 · Cited by 57
Self-citationPauses for Detection of Alzheimer’s Disease
Yuan, J., Cai, X., Bian, Y., Zheng, Y., & Church, K. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2020.624488 · Cited by 49
Agbavor, F., & Liang, H. (2022) · Brain Sciences · doi:10.3390/brainsci13010028 · Cited by 47
Geng, M., Xie, X., Ye, Z., Wang, T., Li, G., Hu, S., Liu, X., & Meng, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3195113 · Cited by 48
Alzheimer's disease recognition from spontaneous speech using large language models
Bang, J., Han, S., & Kang, B. (2024) · ETRI Journal · doi:10.4218/etrij.2023-0356 · Cited by 49
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition
Jin, Z., Geng, M., Deng, J., Wang, T., Hu, S., Li, G., & Liu, X. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3323888 · Cited by 52
Multiscale System for Alzheimer’s Dementia Recognition Through Spontaneous Speech
Edwards, E., Dognin, C., Bollepalli, B., & Singh, M. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2781 · Cited by 48
Automated Recognition of Alzheimer’s Dementia Using Bag-of-Deep-Features and Model Ensembling
Syed, Z. S., Syed, M. S. S., Lech, M., & Pirogova, E. (2021) · IEEE Access · doi:10.1109/access.2021.3090321 · Cited by 50
Guo, Y., Li, C., Roan, C., Pakhomov, S., & Cohen, T. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.642517 · Cited by 44
Towards Computer-Based Automated Screening of Dementia Through Spontaneous Speech
Chlasta, K., & Wołk, K. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.623237 · Cited by 42
Speech and language processing with deep learning for dementia diagnosis: A systematic review
Shi, M., Cheung, G., & Shahamiri, S. R. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115538 · Cited by 46
Longitudinal Speech Biomarkers for Automated Alzheimer's Detection
Laguarta, J., Hueto, F., Rajasekaran, P., Sarma, S. E., & Subirana, B. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.624694 · Cited by 50
Identification of digital voice biomarkers for cognitive health
Lin, H., Karjadi, C., Ang, T. F. A., Joshi, P. S., McManus, C., Alhanai, T., Glass, J. R., & Au, R. (2020) · Exploration of Medicine · doi:10.37349/emed.2020.00028 · Cited by 42
Evaluating voice-assistant commands for dementia detection
Liang, X., Batsis, J. A., Zhu, Y., Driesse, T. M., Roth, R. M., Kotz, D., & MacWhinney, B. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2021.101297 · Cited by 43
Self-citationNoninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review
Qi, X., Zhou, Q., Dong, J., & Bao, W. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1224723 · Cited by 44
Self-Supervised ASR Models and Features for Dysarthric and Elderly Speech Recognition
Hu, S., Xie, X., Geng, M., Jin, Z., Deng, J., Li, G., Wang, Y., Cui, M., Wang, T., Meng, H. M. L., et al. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3422839 · Cited by 40
Meghanani, A., Anoop, C. S., & Ramakrishnan, A. G. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.624558 · Cited by 39
A Deep Learning-Based Multimodal Architecture to predict Signs of Dementia
Ortiz-Pérez, D., Ruiz-Ponce, P., Tomás, D., García‐Rodríguez, J., Vizcaya-Moreno, M. F., & Leo, M. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126413 · Cited by 41
Automatic depression severity assessment with deep learning using parameter-efficient tuning
Lau, C., Zhu, X., & Chan, W.-Y. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1160291 · Cited by 39
The INESC-ID Multi-Modal System for the ADReSS 2020 Challenge
Pompili, A., Rolland, T., & Abad, A. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2833 · Cited by 39
Ye, Z., Hu, S., Li, J., Xie, X., Geng, M., Yu, J., Xu, J., Xue, B., Liu, S., Liu, X., et al. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413634 · Cited by 36
Liu, J., Fu, F., Li, L., Yu, J., Zhong, D., Zhu, S., Zhou, Y., Liu, B., & Li, J. (2023) · Brain Sciences · doi:10.3390/brainsci13030477 · Cited by 36
Li, J., Yu, J., Ye, Z., Wong, K. H., Mak, M., Mak, B. K.-W., Liu, X., & Meng, H. M. L. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414147 · Cited by 35
Jang, H., Soroski, T., Rizzo, M., Barral, O., Harisinghani, A., Newton‐Mason, S. M., Granby, S., Vasco, T. D. C., Lewis, C., Tutt, P., et al. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.716670 · Cited by 42
Ivanova, O., Meilán, J. J. G., Martínez‐Sánchez, F., Martínez‐Nicolás, I., Llorente, T. E., & Carcavilla-González, N. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101341 · Cited by 35
Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network
Liu, N., Yuan, Z., & Tang, Q. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2021.835960 · Cited by 32
Language-related features for early detection of Alzheimer Disease
Ammar, R. B., & BenAyed, Y. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.09.071 · Cited by 32
Priyadarshinee, P., Clarke, C. J., Melechovský, J., Lin, C. M. Y., Balamurali, B. T., & Chen, J. (2023) · Applied Sciences · doi:10.3390/app13074244 · Cited by 33
Applications of artificial intelligence in dementia research
Tsoi, K. K. F., Jia, P., Dowling, N. M., Titiner, J. R., Wagner, M., Capuano, A. W., & Donohue, M. (2022) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2022.10 · Cited by 33
García, A. M., Johann, F., Echegoyen, R. E., Calcaterra, C., Riera, P., Belloli, L. M. L., & Carrillo, F. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02240-z · Cited by 29
Detecting Dementia from Face-Related Features with Automated Computational Methods
Zheng, C., Bouazizi, M., Ohtsuki, T. O., Kitazawa, M., Horigome, T., & Kishimoto, T. (2023) · Bioengineering · doi:10.3390/bioengineering10070862 · Cited by 29
Augmented Datasheets for Speech Datasets and Ethical Decision-Making
Papakyriakopoulos, O., Choi, A. S. G., Thong, W. E., Zhao, D., Andrews, J. T. A., Bourke, R., Xiang, A., & Koenecke, A. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594049 · Cited by 36
Multimodal deep learning for dementia classification using text and audio
Lin, K., & Washington, P. Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-64438-1 · Cited by 26
Ilias, L., & Askounis, D. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110834 · Cited by 26
Luz, S. F., Haider, F., Fromm, D., Lazarou, I., Kompatsiaris, I. Y., & MacWhinney, B. (2024) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2024.3378595 · Cited by 26
Self-citationDeep ensemble learning with transformer models for enhanced Alzheimer’s disease detection
Latif, S., Islam, N. U., Uddin, Z., Cheema, K. M., Ahmed, S. S., & Khan, M. F. (2025) · Scientific Reports · doi:10.1038/s41598-025-08362-y · Cited by 25
Exploring MMSE Score Prediction Using Verbal and Non-Verbal Cues
Farzana, S., & Parde, N. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-3085 · Cited by 26
He, R., Chapin, K., Al‐Tamimi, J., Bel, N., Marquié, M., Rosende‐Roca, M., Pytel, V., Tartari, J. P., Alegret, M., Sanabria, Á., et al. (2023) · American Journal of Speech-Language Pathology · doi:10.1044/2023_ajslp-22-00403 · Cited by 23
Dementia Detection using Transformer-Based Deep Learning and Natural Language Processing Models
Saltz, P., Lin, S. Y., Cheng, S. C., & Si, D. (2021) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi52183.2021.00094 · Cited by 23
Pan, Y., Mirheidari, B., Reuber, M., Venneri, A., Blackburn, D. J., & Christensen, H. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2698 · Cited by 23
Data Augmentation for Dementia Detection in Spoken Language.
Woszczyk, D. C., Hedlikova, A., Akman, A., Demetriou, S., & Schuller, B. W. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10210 · Cited by 21
Zhang, M., Tang, E., Ding, H., & Zhang, Y. (2024) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2024_jslhr-24-00157 · Cited by 22
Lee, B., Bang, J., Song, H. J., & Kang, B. (2025) · Scientific Reports · doi:10.1038/s41598-024-82597-z · Cited by 22
Alzheimer's Dementia Detection from Audio and Language Modalities in Spontaneous Speech
Campbell, E. L., Docío-Fernández, L., Jiménez-Raboso, J., & Mateo, C. G. (2021) · IberSPEECH · doi:10.21437/iberspeech.2021-57 · Cited by 19
Li, C., Knopman, D. S., Xu, W., Cohen, T., & Pakhomov, S. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.131 · Cited by 22
Gagliardi, G. (2023) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.12870 · Cited by 25
Villatoro-Tello, E., Dubagunta, S. P., Fritsch, J., Ramírez-de-la-Rosa, G., Motlíček, P., & Magimai.-Doss, M. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1288 · Cited by 22
A Path Signature Approach for Speech-Based Dementia Detection
Pan, Y., Lu, M., Shi, Y., & Zhang, H. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3291651 · Cited by 20
Ten years of image analysis and machine learning competitions in dementia
Bron, E. E., Klein, S., Reinke, A., Papma, J. M., Maier-Hein, L., Alexander, D. C., & Oxtoby, N. P. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119083 · Cited by 17
Ortiz-Pérez, D., Benavent-Lledó, M., Rodriguez-Juan, J., García‐Rodríguez, J., & Tomás, D. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114264 · Cited by 20
Dementia Speech Dataset Creation and Analysis in Indic Languages—A Pilot Study
Vekkot, S., Prakash, N. N. V. S., Reddy, T. S. E., Sripathi, S. R., Lalitha, S., Gupta, D., Zakariah, M., & Alotaibi, Y. A. (2023) · IEEE Access · doi:10.1109/access.2023.3334790 · Cited by 17
Useful blunders: Can automated speech recognition errors improve downstream dementia classification?
Li, C., Xu, W., Cohen, T., & Pakhomov, S. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104598 · Cited by 25
Pan, Y., Mirheidari, B., Tu, Z., O’Malley, R., Walker, T., Venneri, A., Reuber, M., Blackburn, D. J., & Christensen, H. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2684 · Cited by 17
Paralinguistic and linguistic fluency features for Alzheimer's disease detection
Campbell, E. L., Mesía, R. Y., Docío-Fernández, L., & Mateo, C. G. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101198 · Cited by 16
Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls
Millington, T., & Luz, S. F. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.649508 · Cited by 18
Self-citationFavaro, A., Motley, C., Cao, T., Iglesias, M., Butala, A., Oh, E. S., Stevens, R. D., Villalba, J., Dehak, N., & Moro-Velázquez, L. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10022435 · Cited by 19
Multilingual Prediction of Cognitive Impairment with Large Language Models and Speech Analysis
Agbavor, F., & Liang, H. (2024) · Brain Sciences · doi:10.3390/brainsci14121292 · Cited by 15
Farzana, S., Deshpande, A., & Parde, N. (2022) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2022.bionlp-1.4 · Cited by 16
Zhang, Z., Wang, T., Hu, Z., Yang, L.-Z., & Li, H. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3509620 · Cited by 17
A longitudinal multi-modal dataset for dementia monitoring and diagnosis
Gkoumas, D., Wang, B., Tsakalidis, A., Wolters, M. K., Purver, M., Zubiaga, A., & Liakata, M. (2024) · Language Resources and Evaluation · doi:10.1007/s10579-023-09718-4 · Cited by 18
Müller, P., Dietz, M., Schiller, D., Thomas, D., Lindsay, H., Gebhard, P., André, E., & Bulling, A. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3551589 · Cited by 15
Multi-Modal Detection of Alzheimer's Disease from Speech and TextPreprint
Mittal, A., Sahoo, S., Datar, A., Kadiwala, J., Shalu, H., & Mathew, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00096 · Cited by 23
Speech Emotion Recognition in Mental Health: Systematic Review of Voice-Based Applications
Jordan, E., Terrisse, R., Lucarini, V., Alrahabi, M., Krebs, M., Desclés, J., & Lemey, C. (2025) · JMIR Mental Health · doi:10.2196/74260 · Cited by 19
Alzheimer’s Dementia Detection Using Acoustic & Linguistic Features and Pre-trained BERT
Valsaraj, A., Madala, I., Garg, N., & Baths, V. (2021) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi53840.2021.9654804 · Cited by 19
Meerza, S. I. A., Li, Z., Liu, L., Zhang, J., & Liu, J. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871204 · Cited by 16
Yamada, Y., Shinkawa, K., Nemoto, M., & Arai, T. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.712251 · Cited by 14
Gómez-Zaragozá, L., Wills, S., Tejedor-Garcı́a, C., Marín‐Morales, J., Alcañíz, M., & Strik, H. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-1734 · Cited by 13
Speech as a Biomarker for Disease Detection
Botelho, C., Abad, A., Schultz, T., & Trancoso, I. M. (2024) · IEEE Access · doi:10.1109/access.2024.3506433 · Cited by 20
A Review of Machine Learning Approaches for Non-Invasive Cognitive Impairment Detection
Alsuhaibani, M., Fard, A. P., Sun, J., Poor, F. F., Pressman, P., & Mahoor, M. H. (2025) · IEEE Access · doi:10.1109/access.2025.3555176 · Cited by 20
Oiza-Zapata, I., & Gallardo-Antolín, A. (2025) · Electronics · doi:10.3390/electronics14112248 · Cited by 16
Tang, J., Chen, W., Chang, X., Watanabe, S., & MacWhinney, B. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-2191 · Cited by 19
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
