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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 101–200 of 274
Pan, Y., Mirheidari, B., Blackburn, D. J., & Christensen, H. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3533363 · Cited by 13
Natural language processing‐based classification of early Alzheimer's disease from connected speech
Balabin, H., Tamm, B., Spruyt, L., Dusart, N., Kabouche, I., Eycken, E., Statz, K., Meyer, S. D., hamme, H. V., Dupont, P. J., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14530 · Cited by 13
Hu, J., Li, J., Zeng, Y., Yang, D., Liang, D., Meng, H. M. L., & Ma, X. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642960 · Cited by 15
Multi-Task Estimation of Age and Cognitive Decline from Speech
Pan, Y., Nallanthighal, V. S., Blackburn, D. J., Christensen, H., & Härmä, A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414642 · Cited by 12
Improving Early Prognosis of Dementia Using Machine Learning Methods
Katsimpras, G., Aisopos, F., Garrard, P., Vidal, M.-E., & Παλιούρας, Γ. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3502433 · Cited by 13
Multimodal Data Fusion for Automatic Detection of Alzheimer’s Disease
Krstev, I., Pavikjevikj, M., Toshevska, M., & Gievska, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06018-2_6 · Cited by 12
A Methodology for Emergency Calls Severity Prediction: From Pre-processing to BERT-Based Classifiers
Kanaan, M. A., Couchot, J.-F., Guyeux, C., Laiymani, D., Atéchian, T., & Darazi, R. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-34111-3_28 · Cited by 16
Dong, Z., Xu, W., Xu, X., & Zhang, Z. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3546950 · Cited by 12
Rizzo, M. (2025) · Annals of Neurology · doi:10.1002/ana.27230 · Cited by 13
Interpreting acoustic features for the assessment of Alzheimer’s disease using ForestNet
Pérez-Toro, P. A., Rodríguez-Salas, D., Arias‐Vergara, T., Klumpp, P., Schuster, M. E., Nöth, E., Orozco‐Arroyave, J. R., & Maier, A. (2022) · Smart Health · doi:10.1016/j.smhl.2022.100347 · Cited by 11
A Multimodal Approach for Dementia Detection from Spontaneous Speech with Tensor Fusion Layer
Ilias, L., Askounis, D., & Psarras, J. E. (2022) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi56158.2022.9926818 · Cited by 13
Alzheimer's Dementia Detection through Spontaneous Dialogue with Proactive Robotic Listeners
Li, Y., Lai, C., Lala, D., Inoue, K., & Kawahara, T. (2022) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri53351.2022.9889375 · Cited by 12
Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous SpeechPreprint
Qin, Y. H., Liu, W., Peng, Z., Ng, S.-I., Li, J., Hu, H., & Lee, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.01493 · Cited by 17
Spoken language-based automatic cognitive assessment of stroke survivors
Mirheidari, B., Bell, S., Harkness, K., Blackburn, D. J., & Christensen, H. (2024) · Language and Health · doi:10.1016/j.laheal.2024.01.001 · Cited by 12
Addlesee, A., & Eshghi, A. (2024) · Frontiers in Dementia · doi:10.3389/frdem.2024.1343052 · Cited by 11
Alzheimer's Dementia Detection from Audio and Text ModalitiesPreprint
Campbell, E. L., Docío-Fernández, L., Jiménez-Raboso, J., & Mateo, C. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.04617 · Cited by 13
Optimizing and Evaluating Pre- Trained Large Language Models for Alzheimer's Disease Detection
Casu, F., Grosso, E., Lagorio, A., & Trunfio, G. A. (2024) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp62718.2024.00046 · Cited by 11
Advanced speech biomarker integration for robust Alzheimer’s disease diagnosis
Hallani, A. E., Chakhtouna, A., & Adib, A. (2025) · Annals of Telecommunications · doi:10.1007/s12243-025-01073-5 · Cited by 10
TaghiBeyglou, B., & Rudzicz, F. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100046 · Cited by 12
Alzheimer’s Dementia Recognition Using Multimodal Fusion of Speech and Text Embeddings
Pandey, S., Shekhawat, H. S., Bhasin, S., Jasuja, R., & Prasanna, S. R. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98404-5_64 · Cited by 9
Slovak database of speech affected by neurodegenerative diseases
Rusko, M., Sabo, R., Trnka, M., Zimmermann, A., Malaschitz, R., Ružický, E., Brandoburová, P., Kevická, V., & Škorvánek, M. (2024) · Scientific Data · doi:10.1038/s41597-024-04171-6 · Cited by 21
Speech as a Window to the Brain: Innovative Techniques in Early Alzheimer’s Disease Detection
Hallani, A. E., Chakhtouna, A., & Adib, A. (2024) · International Symposium on Signal, Image, Video and Communications (ISIVC) · doi:10.1109/isivc61350.2024.10577888 · Cited by 8
Modality fusion using auxiliary tasks for dementia detection
Shao, H., Pan, Y., Wang, Y., & Zhang, Y. (2025) · Computer Speech & Language · doi:10.1016/j.csl.2025.101814 · Cited by 8
CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection
Duan, J., Wei, F., Liu, J., Li, H., Liu, T., & Wang, J. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.114 · Cited by 8
Linguistic-based Mild Cognitive Impairment detection using Informative Loss
Fard, A. P., Mahoor, M. H., Alsuhaibani, M., & Dodge, H. H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108606 · Cited by 8
Affect Recognition Through Scalogram and Multi-Resolution Cochleagram Features
Haider, F., & Luz, S. F. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1761 · Cited by 8
Self-citationAn Automatic System for Dementia Detection using Acoustic and Linguistic Features
González-Atienza, M., Peinado, A. M., & González, J. A. (2021) · IberSPEECH · doi:10.21437/iberspeech.2021-56 · Cited by 8
Analysis of Voice Biomarkers for the Detection of Cognitive Impairment
Pacheco-Lorenzo, M. R., Christensen, H., Anido, L., Iglesias, M. J. F., & Valladares‐Rodríguez, S. (2024) · IEEE Access · doi:10.1109/access.2024.3442431 · Cited by 7
Speaker Diarization based on Deep Learning Techniques: A Review
Al-Hadithy, T. M., Frikha, M., & Maseer, Z. K. (2022) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit56059.2022.9932710 · Cited by 7
Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features
Liu, N., Yuan, Z., Chen, Y., Liu, C., & Wang, L. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1122799 · Cited by 9
Automatic selection of spoken language biomarkers for dementia detection
Ke, X., Mak, M., & Meng, H. M. L. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.10.018 · Cited by 7
Integrating Fine-Tuned LLM With Acoustic Features for Enhanced Detection of Alzheimer's Disease
Casu, F., Lagorio, A., Ruiu, P., Trunfio, G. A., & Grosso, E. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3566615 · Cited by 7
Robustness and Sensitivity of BERT Models Predicting Alzheimer’s Disease from Text
Novikova, J. (2021) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/2021.wnut-1.37 · Cited by 11
A Systematic Review of Alzheimer's disease detection based on speech and natural language processing
Sevcik, A., & Rusko, M. (2022) · International Conference Radioelektronika · doi:10.1109/radioelektronika54537.2022.9764938 · Cited by 5
Alzheimer's Disease Detection Using Acoustic And Linguistic Features
Deng, H., Liu, H., Zhou, Y., & Gan, L. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00337 · Cited by 7
Are Interaction Patterns Helpful for Task-Agnostic Dementia Detection? An Empirical Exploration
Farzana, S., & Parde, N. (2022) · SIGDIAL Conferences · doi:10.18653/v1/2022.sigdial-1.18 · Cited by 11
Swin-BERT: A Feature Fusion System designed for Speech-based Alzheimer's Dementia Detection
Pan, Y., Shi, Y., Zhang, Y., & Lu, M. (2024) · doi:10.1145/3700410.3702116 · Cited by 6
Can Automated Speech Recognition Errors Provide Valuable Clues for Alzheimer’s Disease Detection?
Liu, Y.-L., Feng, R., Lu, Y.-X., Chen, J., Ai, Y., Yuan, J., & Ling, Z.-H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890694 · Cited by 7
Speech and language in healthy ageing and Alzheimer’s dementia
García, A. M. (2026) · Nature Reviews Psychology · doi:10.1038/s44159-026-00553-2 · Cited by 10
Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language
Farzana, S., & Parde, N. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.668 · Cited by 5
Shakeri, A., Freja, S. A., Hallaj, Y., & Farmanbar, M. (2024) · International Conference on Applied Artificial Intelligence (ICAPAI) · doi:10.1109/icapai61893.2024.10541296 · Cited by 6
Niemelä, M., von Bonsdorff, M. B., Äyrämö, S., Kärkkäinen, T., & Wellbeing Services County of Central Finland, F. (2024) · Applied Computing and Intelligence · doi:10.3934/aci.2024004 · Cited by 8
Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings
Sarawgi, U., Khincha, R., Zulfikar, W. D., Ghosh, S., & Maes, P. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534161 · Cited by 7
Towards Interpretability of Speech Pause in Dementia Detection Using Adversarial Learning
Zhu, Y., Tran, B., Liang, X., Batsis, J. A., & Roth, R. M. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747006 · Cited by 6
Monica, G. M., & Martínez‐Tomás, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06242-1_42 · Cited by 6
Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments Using Acoustic Features
Braun, F. M., Erzigkeit, A., Lehfeld, H., Hillemacher, T., Riedhammer, K., & Bayerl, S. P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16270-1_36 · Cited by 6
Pre-training and ensembling based Alzheimer’s disease detection
Xu, F., Zheng, Q., Shi, J., Yan, K., & Wang, M. (2023) · Technology and Health Care · doi:10.3233/thc-230571 · Cited by 6
Static vs. Dynamic Modelling of Acoustic Speech Features for Detection of Dementia
Syed, M. S. S., Syed, Z. S., Pirogova, E., & Lech, M. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111082 · Cited by 6
Combining Prosodic, Voice Quality and Lexical Features to Automatically Detect Alzheimer's DiseasePreprint
Farrús, M., & Codina, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09272 · Cited by 10
de la Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-200888 · Cited by 5
Self-citationChen, C.-P., & Li, J.-L. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782161 · Cited by 5
Ceyhan, B., Bek, S. A., & Önal-Süzek, T. (2024) · Journal of Aging Research and Lifestyle · doi:10.14283/jarlife.2024.6 · Cited by 5
Sigona, F., Radicioni, D. P., Fivela, B. G., Colla, D., Delsanto, M., Mensa, E., Bolioli, A., & Vigorelli, P. (2024) · Computer Speech & Language · doi:10.1016/j.csl.2024.101691 · Cited by 7
Pahar, M., Tao, F., Mirheidari, B., Pevy, N., Bright, R., Gadgil, S., Sproson, L., Braun, D., Illingworth, C. H., Blackburn, D. J., et al. (2025) · doi:10.1109/cihm64979.2025.10969487 · Cited by 5
Transformer-Based Deep Learning Approaches for Speech-Based Dementia Detection: A Systematic Review
Mobtahej, P., Gouron, S., Javaheri, R., Le, D. N., El‐Khoury, A., Naing, T. H., & Sajjadi, S. A. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3595781 · Cited by 5
Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-Encoders
Qi, J., & hamme, H. V. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2180 · Cited by 5
Igarashi, T., Umeda‐Kameyama, Y., Kojima, T., Akishita, M., & Nihei, M. (2023) · Sensors · doi:10.3390/s23115346 · Cited by 5
A Review of Alzheimer’s Disease Detection from Spontaneous Speech and Text
Vrindha, M. K., Geethu, V., Anurenjan, P. R., Deepak, S., & Sreeni, K. G. (2023) · International Conference on Control, Communication and Computing (ICCC) · doi:10.1109/iccc57789.2023.10165507 · Cited by 6
Who needs context? Classical techniques for Alzheimer’s disease detection
TaghiBeyglou, B., & Rudzicz, F. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.13 · Cited by 5
Training Models on Oversampled Data and a Novel Multi-class Annotation Scheme for Dementia Detection
Abdelhalim, N., Abdelhalim, I., & Batista-Navarro, R. (2023) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2023.clinicalnlp-1.15 · Cited by 5
Detection of Dementia using Machine Learning Algorithms
Ramyasri, M. M., Yoga, M., Vetri Vendhan, S., Pramoth Kumar, M., & Karthickeyan, S. (2024) · International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS) · doi:10.1109/icc-robins60238.2024.10534009 · Cited by 5
Kerz, E., Qiao, Y., & Wiechmann, D. (2021) · RWTH Publications (RWTH Aachen) · Cited by 6
Vocal markers from sustained phonation in Huntington's DiseasePreprint
Riad, R., Titeux, H., Lemoine, L., Montillot, J., Bagnou, J. H., Cao, X., Emmanuel Dupoux, A., & Bachoud‐Lévi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05365 · Cited by 10
Flick, G., & Ostrand, R. (2025) · Journal of Speech Language and Hearing Research · doi:10.1044/2025_jslhr-24-00297 · Cited by 6
Unveiling 30 years of research on speech biomarker of dementia using text mining
Oh, C., & Park, M. S. (2025) · Digital Health · doi:10.1177/20552076251360901 · Cited by 4
Qian, C., Di, J., & Li, J. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995234 · Cited by 4
Detecting Dementia from Long Neuropsychological Interviews
Dawalatabad, N., Gong, Y., Khurana, S., Au, R., & Glass, J. R. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.386 · Cited by 4
Deep Learning-Based Dementia Prediction Using Multimodal Data
Ortiz-Pérez, D., Ruiz-Ponce, P., Tomás, D., & Garcia-Rodriguez, J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-18050-7_25 · Cited by 4
ADReFV : Face video dataset based on human‐computer interaction for Alzheimer's disease recognition
Xu, T., Wang, X., Lun, X., Pan, H., & Wang, Z. (2022) · Computer Animation and Virtual Worlds · doi:10.1002/cav.2127 · Cited by 6
Gkoumas, D., Purver, M., & Liakata, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.986 · Cited by 5
Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split EnsemblesPreprint
Sarawgi, U., Zulfikar, W. D., Khincha, R., & Maes, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12406 · Cited by 6
Alzheimers Dementia Detection using Acoustic & Linguistic features and Pre-Trained BERT.Preprint
Valsaraj, A., Madala, I., Garg, N., & Baths, V. (2021) · arXiv (Cornell University) · Cited by 4
Ortiz-Pérez, D., Benavent-Lledó, M., Garcia-Rodriguez, J., Tomás, D., & Vizcaya-Moreno, M. F. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113787 · Cited by 6
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Hallani, A. E., Chakhtouna, A., & Adib, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-01536-5_31 · Cited by 3
Pahar, M., Mirheidari, B., Illingworth, C. H., Braun, D., Tao, F., Sproson, L., Blackburn, D. J., & Christensen, H. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3624043 · Cited by 3
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Braun, F. M., Förstel, M., Oppermann, B., Erzigkeit, A., Lehfeld, H., Hillemacher, T., & Riedhammer, K. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10436 · Cited by 3
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Mirheidari, B., Malley, R. O., Blackburn, D. J., & Christensen, H. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389623 · Cited by 3
Predictive deep learning models for cognitive risk using accessible data
Karako, K. (2024) · BioScience Trends · doi:10.5582/bst.2024.01026 · Cited by 2
Adarkhi, K. R., Patel, H., & Patel, R. K. (2024) · International Research Journal on Advanced Engineering and Management (IRJAEM) · doi:10.47392/irjaem.2024.0138 · Cited by 3
Qi, K., Shi, J., Summerour, C. E., Batsis, J. A., & Liang, X. (2024) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom60970.2024.10880714 · Cited by 4
Task-Oriented Feature Representation for Spontaneous Speech of AD Patients
Li, J., & Huang, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91415-8_5 · Cited by 3
Automatic Classification of Dementia Using Text and Speech Data
Han, H. J., Suhas, B. N., Qiu, L., & Abdullah, S. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-14771-5_29 · Cited by 3
Detecting Dementia from Transcribed Speech in Slovak using Pre-trained BERT Models
Staš, J., Hládek, D., & Kopnický, A. (2024) · International Conference Radioelektronika · doi:10.1109/radioelektronika61599.2024.10524067 · Cited by 2
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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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