A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech
Open accessAbstract
Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Asgari et al. achieved over 80% classification accuracy for MCI versus healthy controls using LIWC categories.
“They were able to achieve over 80% classification accuracy by selecting LIWC categories that best represented the difference in the MCI and healthy control datasets.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [7]
First-order lexeme-level features are useful biomarkers for detecting the presence or predicting the onset of MCI, AD, CTE, and potentially other conditions.
“It is clear from the literature that first-order lexeme-level features, i.e. those related to lexical diversity and density, are useful biomarkers for detecting the presence or predicting the onset of a variety of conditions, such as MCI, AD, CTE, and potentially several others.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,010 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,803 · see citing works
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,003 · see citing works
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168 · see citing works
Praat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417 · see citing works
A new rating scale for Alzheimer's disease
(1984) · American Journal of Psychiatry · doi:10.1176/ajp.141.11.1356 · Cited by 4,175 · see citing works
Naive (Bayes) at forty: The independence assumption in information retrieval
Lewis, D. D. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026666 · Cited by 2,140 · see citing works
Language performance in Alzheimer's disease and mild cognitive impairment: A comparative review
Taler, V., & Phillips, N. A. (2008) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/13803390701550128 · Cited by 529 · see citing works
Spoken Language Derived Measures for Detecting Mild Cognitive Impairment
Roark, B., Mitchell, M., Hosom, J.-P., Hollingshead, K., & Kaye, J. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2112351 · Cited by 355 · see citing works
Innovative diagnostic tools for early detection of Alzheimer's disease
Laske, C., Sohrabi, H. R., Frost, S. M., López-de-Ipiña, K., Garrard, P., Buscema, M., Dauwels, J., Soekadar, S. R., Mueller, S., Linnemann, C., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.06.004 · Cited by 316 · see citing works
The effects of very early Alzheimer's disease on the characteristics of writing by a renowned author
Garrard, P. (2004) · Brain · doi:10.1093/brain/awh341 · Cited by 281 · see citing works
Forbes-McKay, K. E., & Venneri, A. (2005) · Neurological Sciences · doi:10.1007/s10072-005-0467-9 · Cited by 261 · see citing works
Language function in senile dementia
Bayles, K. A. (1982) · Brain and Language · doi:10.1016/0093-934x(82)90086-4 · Cited by 247 · see citing works
Automated classification of primary progressive aphasia subtypes from narrative speech transcripts
Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014) · Cortex · doi:10.1016/j.cortex.2012.12.006 · Cited by 229 · see citing works
López-de-Ipiña, K., Alonso, J.-B., Travieso, C., Solé-Casals, J., Egiraun, H., Faundez-Zanuy, M., Ezeiza, A., Barroso, N., Ecay-Torres, M., Martinez-Lage, P., et al. (2013) · Sensors · doi:10.3390/s130506730 · Cited by 190 · see citing works
Temporal parameters of spontaneous speech in Alzheimer's disease
Hoffmann, I., Németh, D., Dye, C. D., Pákáski, M., Irinyi, T., & Kálmán, J. (2009) · International Journal of Speech-Language Pathology · doi:10.3109/17549500903137256 · Cited by 180 · see citing works
Self-citationAided diagnosis of dementia type through computer-based analysis of spontaneous speech
Jarrold, W., Peintner, B., Wilkins, D., Vergryi, D., Richey, C., Gorno-Tempini, M. L., & Ogar, J. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3204 · Cited by 178 · see citing works
Phonological and Articulatory Impairment in Alzheimer's Disease: A Case Series
Croot, K., Hodges, J. R., Xuereb, J. H., & Patterson, K. E. (2000) · Brain and Language · doi:10.1006/brln.2000.2357 · Cited by 133 · see citing works
Dementia Assessment in Primary Care: Results From a Study in Three Managed Care Systems
Boise, L., Neal, M. B., & Kaye, J. A. (2004) · The Journals of Gerontology Series A · doi:10.1093/gerona/59.6.m621 · Cited by 133 · see citing works
López-de-Ipiña, K., Alonso, J. B., Solé-Casals, J., Barroso, N., Henriquez, P., Faundez-Zanuy, M., Travieso, C. M., Ecay-Torres, M., Martínez-Lage, P., & Eguiraun, H. (2015) · Cognitive Computation · doi:10.1007/s12559-013-9229-9 · Cited by 132 · see citing works
Konig, A., Satt, A., Sorin, A., Hoory, R., Derreumaux, A., David, R., & Robert, P. H. (2018) · Current Alzheimer Research · doi:10.2174/1567205014666170829111942 · Cited by 119 · see citing works
Distinct Patterns of Spontaneous Speech Deterioration: An Early Predictor of Alzheimer's Disease
Forbes‐McKay, K., Venneri, A., & Shanks, M. F. (2002) · Brain and Cognition · doi:10.1006/brcg.2001.1377 · Cited by 95 · see citing works
Automatic detection of mild cognitive impairment from spontaneous speech using ASR
Tóth, L., Gosztolya, G., Vincze, V., Hoffmann, I., Szatlóczki, G., Biró, E. M., Zsura, F., Pákáski, M., & Kálmán, J. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-568 · Cited by 90 · see citing works
Self-citationPhone recognition with hierarchical convolutional deep maxout networks
Tóth, L. (2015) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-015-0068-3 · Cited by 88 · see citing works
Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse
Garrard, P., Rentoumi, V., Gesierich, B., Miller, B., & Gorno-Tempini, M. L. (2014) · Cortex · doi:10.1016/j.cortex.2013.05.008 · Cited by 87 · see citing works
Speech-based automatic and robust detection of very early dementia
Satt, A., Hoory, R., König, A., Aalten, P., & Robert, P. H. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-544 · Cited by 76 · see citing works
Santos, V. D., Thomann, P. A., Wüstenberg, T., Seidl, U., Essig, M., & Schröder, J. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-100156 · Cited by 73 · see citing works
Fully automated neuropsychological assessment for detecting mild cognitive impairment
Lehr, M., Prud’hommeaux, E. T., Izhak Shafran, J., & Brian Roark, A. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-306 · Cited by 73 · see citing works
Lopez-de-Ipina, K., Martinez-de-Lizarduy, U., Calvo, P. M., Mekyska, J., Beitia, B., Barroso, N., Estanga, A., Tainta, M., & Ecay-Torres, M. (2018) · Current Alzheimer Research · doi:10.2174/1567205014666171120143800 · Cited by 65 · see citing works
Acoustic Markers Associated with Impairment in Language Processing in Alzheimer's Disease
Meilán, J. J. G., Martínez‐Sánchez, F., Carro, J., Sánchez, J. A., & Pérez, E. (2012) · The Spanish Journal of Psychology · doi:10.5209/rev_sjop.2012.v15.n2.38859 · Cited by 64 · see citing works
Neuropsychologische Profile in der Demenzdiagnostik: Eine Untersuchung mit der CERAD-NP-Testbatterie
Barth, S., Schönknecht, P., Pantel, J., & Schröder, J. (2005) · Fortschritte der Neurologie · Psychiatrie · doi:10.1055/s-2004-830249 · Cited by 60 · see citing works
Effects of ApoE genotype and mild cognitive impairment on implicit learning
Negash, S., Petersen, L. E., Geda, Y. E., Knopman, D. S., Boeve, B. F., Smith, G. E., Ivnik, R. J., Howard, D. V., Howard, J. H., & Petersen, R. C. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.04.004 · Cited by 60 · see citing works
Automatic speech recognition in the diagnosis of primary progressive aphasia
Fraser, K., Rudzicz, F., Graham, N. L., & Rochon, E. A. (2013) · Cited by 52 · see citing works
Voice Markers of Lexical Access in Mild Cognitive Impairment and Alzheimer's Disease
Meilán, J. J. G., Martínez‐Sánchez, F., Carro, J., Carcavilla-González, N., & Ivanova, O. (2017) · Current Alzheimer Research · doi:10.2174/1567205014666170829112439 · Cited by 52 · see citing works
Non‐literal language deficits in mild cognitive impairment
Cardoso, S., Silva, D., Marôco, J. P., de Mendonça, A., & Guerreiro, M. (2014) · Psychogeriatrics · doi:10.1111/psyg.12101 · Cited by 36 · see citing works
Manouilidou, C., Dolenc, B., Marvin, T., & Pirtošek, Z. (2015) · Clinical Linguistics & Phonetics · doi:10.3109/02699206.2015.1102970 · Cited by 35 · see citing works
BEA – A multifunctional Hungarian spoken language database
Gósy, M. (2013) · Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) · Cited by 33 · see citing works
Towards automatic transcription of large spoken archives - English ASR for the MALACH project
Ramabhadran, B., Huang, J., & Picheny, M. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2003.1198756 · Cited by 30 · see citing works
Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech
Kato, S., Homma, A., & Sakuma, T. (2017) · Current Alzheimer Research · doi:10.2174/1567205014666171120144343 · Cited by 27 · see citing works
Early Diagnosis of Alzheimer’s Type Dementia Using Continuous Speech Recognition
Baldas, V., Lampiris, C., Capsalis, C. N., & Koutsouris, D. (2011) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-20865-2_14 · Cited by 22 · see citing works
Wutzler, A., Becker, R., Lämmler, G., Haverkamp, W., & Steinhagen‐Thiessen, E. (2013) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000350808 · Cited by 14 · see citing works
Burhan, A. M., Anazodo, U., Chung, J. K., Arena, A., Graff‐Guerrero, A., & Mitchell, D. G. V. (2016) · Behavioural Neurology · doi:10.1155/2016/1637392 · Cited by 12 · see citing works
[Early Mental Test--developing a screening test for mild cognitive impairment].
Kálmán, J., Pákáski, M., Hoffmann, I., Drótos, G., Darvas, G., Boda, K., Bencsik, T., Gyimesi, A., Gulyás, Z., Bálint, M., et al. (2013) · PubMed · Cited by 7 · see citing works
Self-citationKim, H., Kang, Y., Yu, K., & Lee, B. (2015) · Eon'eo cheong'gag jang'ae yeon'gu/Communication sciences & disorders · doi:10.12963/csd.15272 · Cited by 6 · see citing works
Spontaneous speech based web screening test for MCI.
Kálmán, J., Hoffmann, I., Hegyi, Á., Szatlóczki, G., & Pákáski, M. (2014) · Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
1 of the 49 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [36]Hall M. Frank E. Holmes G. Pfahringer B. Reutemann P. Witten I. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 2009 11 1 10 18
- [37]Freedman M.I. Leach L. Kaplan E. Winocur G. Shulman K.J. Delis D.C. Clock drawing. Oxford Oxford University Press 1994
- [44]Szabó L. Tarján B. Mihajlik P. Fegyó T. Speeding up error correction in a Hungarian dictation system (in Hungarian Proceedings of MSZNY 182 91 2015
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 · see citing works
Voice for Health: The Use of Vocal Biomarkers from Research to Clinical Practice
Fagherazzi, G., Fischer, A., Ismael, M., & Despotović, V. (2021) · Digital Biomarkers · doi:10.1159/000515346 · Cited by 235 · see citing works
Graham, S. A., Lee, E., Jeste, D. V., Patten, R. V., Twamley, E. W., Nebeker, C., Yamada, Y., Kim, H., & Depp, C. A. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.112732 · Cited by 223 · see citing works
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 191 · see citing works
Predicting mild cognitive impairment from spontaneous spoken utterances
Asgari, M., Kaye, J., & Dodge, H. (2017) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1016/j.trci.2017.01.006 · Cited by 185 · see citing works
Martínez‐Nicolás, I., Llorente, T. E., Martínez‐Sánchez, F., & Meilán, J. J. G. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.620251 · Cited by 182 · see citing works
A systematic literature review of automatic Alzheimer’s disease detection from speech and language
Petti, U., Baker, S., & Korhonen, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa174 · Cited by 168 · see citing works
Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations
Robin, J., Harrison, J., Kaufman, L. D., Rudzicz, F., Simpson, W. S., & Yancheva, M. (2020) · Digital Biomarkers · doi:10.1159/000510820 · Cited by 171 · see citing works
Alzheimer's disease and automatic speech analysis: A review
Pulido, M. L. B., Hernández, J. B. A., Ballester, M. Á. F., González, C. M. T., Mekyska, J., & Smékal, Z. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113213 · Cited by 120 · see citing works
English speech recognition based on deep learning with multiple features
Song, Z. (2019) · Computing · doi:10.1007/s00607-019-00753-0 · Cited by 119 · see citing works
Khatun, S., Morshed, B. I., & Bidelman, G. M. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2911970 · Cited by 109 · see citing works
Deep learning-based speech analysis for Alzheimer’s disease detection: a literature review
Yang, Q., Li, X., Ding, X., Xu, F., & Ling, Z.-H. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-022-01131-3 · Cited by 106 · see citing works
Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers
Fraser, K., Fors, K. L., Eckerström, M., Öhman, F., & Kokkinakis, D. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00205 · Cited by 93 · see citing works
A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders
Voleti, R., Liss, J. M., & Berisha, V. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2952087 · Cited by 96 · see citing works
Filiou, R.-P., Bier, N., Slegers, A., Houzé, B., Belchior, P. D. C., & Brambati, S. M. (2019) · Aphasiology · doi:10.1080/02687038.2019.1608502 · Cited by 92 · see citing works
Yeung, A., Iaboni, A., Rochon, E. A., Lavoie, M., Santiago, C., Yancheva, M., Novikova, J., Xu, M., Robin, J., Kaufman, L. D., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00848-x · Cited by 90 · see citing works
Linguistic features and automatic classifiers for identifying mild cognitive impairment and dementia
Calzà, L., Gagliardi, G., Favretti, R. R., & Tamburini, F. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101113 · Cited by 97 · see citing works
Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T. R., Rizwan, M., Maddikunta, P. K. R., Mahmud, M., Liyanage, M., & Hussain, A. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10153-4 · Cited by 97 · see citing works
Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results
Despotović, V., Ismael, M., Cornil, M., Call, R. M., & Fagherazzi, G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104944 · Cited by 84 · see citing works
Speech- and language-based classification of Alzheimer's disease: a systematic review
Vigo, I., Coelho, L., & Reis, S. (2022) · Bioengineering · doi:10.3390/bioengineering9010027 · Cited by 83 · see citing works
Idrisoglu, A., Dallora, A. L., Anderberg, P., & Berglund, J. S. (2023) · Journal of Medical Internet Research · doi:10.2196/46105 · Cited by 83 · see citing works
Hecker, P., Steckhan, N., Eyben, F., Schuller, B. W., & Arnrich, B. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.842301 · Cited by 78 · see citing works
Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task
König, A., Linz, N., Tröger, J., Wolters, M., Alexandersson, J., & Robert, P. (2018) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000487852 · Cited by 77 · see citing works
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 72 · see citing works
Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment
Robin, J., Xu, M., Kaufman, L. D., & Simpson, W. S. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.749758 · Cited by 68 · see citing works
Hajjar, I. M., Okafor, M. O., Choi, J. D., Moore, E., Abrol, A., Calhoun, V. D., & Goldstein, F. C. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12393 · Cited by 72 · see citing works
Bazarbekov, I., Razaque, A., Ipalakova, M., Yoo, J., Assipova, Z., & Almisreb, A. A. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106023 · Cited by 68 · see citing works
An automatic Alzheimer’s disease classifier based on spontaneous spoken English
Bertini, F., Allevi, D., Lutero, G., Calzà, L., & Montesi, D. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2021.101298 · Cited by 66 · see citing works
Introduction to large language models (LLMs) for dementia care and research
Treder, M. S., Lee, S., & Tsvetanov, K. A. (2024) · Frontiers in Dementia · doi:10.3389/frdem.2024.1385303 · Cited by 63 · see citing works
Chien, Y.-W., Hong, S.-Y., Cheah, W.-T., Yao, L.-H., Chang, Y., & Fu, L. (2019) · Scientific Reports · doi:10.1038/s41598-019-56020-x · Cited by 61 · see citing works
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 58 · see citing works
Yamada, Y., Shinkawa, K., Kobayashi, M., Caggiano, V., Nemoto, M., Nemoto, K., & Arai, T. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-210684 · Cited by 57 · see citing works
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 · see citing works
Martinc, M., Haider, F., Pollak, S., & Luz, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.642647 · Cited by 55 · see citing works
“Toth et al. (2018), for instance, found that filled pauses (sounds like “hmmm,” etc.) could not be reliably detected by human annotators, and that detection improved by using ASR-generated transcriptions.”
InfluentialGuess What We Can Hear—Novel Voice Biomarkers for the Remote Detection of Disease
Sara, J. D. S., Orbelo, D. M., Maor, E., Lerman, L. O., & Lerman, A. (2023) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2023.03.007 · Cited by 53 · see citing works
Çokal, D., Zimmerer, V. C., Turkington, D., Ferrier, N., Varley, R. A., Watson, S., & Hinzen, W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217404 · Cited by 50 · see citing works
Automatic Speech Classifier for Mild Cognitive Impairment and Early Dementia
Bertini, F., Allevi, D., Lutero, G., Montesi, D., & Calzà, L. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3469089 · Cited by 49 · see citing works
Meilán, J. J. G., Martínez‐Sánchez, F., Martínez‐Nicolás, I., Llorente, T. E., & Carro, J. (2020) · Behavioural Neurology · doi:10.1155/2020/4683573 · Cited by 49 · see citing works
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 · see citing works
Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia
Ahamed, F., Shahrestani, S. A., & Cheung, H. (2020) · Sensors · doi:10.3390/s20216031 · Cited by 45 · see citing works
Kobayashi, M., Kosugi, A., Takagi, H., Nemoto, M., Nemoto, K., Arai, T., & Yamada, Y. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-29390-1_4 · Cited by 45 · see citing works
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 46 · see citing works
Automatic Detection of Alzheimer’s Disease Using Spontaneous Speech Only
Chen, J., Ye, J., Tang, F., & Zhou, J. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2002 · Cited by 44 · see citing works
Speech pause distribution as an early marker for Alzheimer’s disease
Pastoriza-Domínguez, P., Torre, I. G., Diéguez-Vide, F., Gómez‐Ruiz, I., Geladó, S., López, J. B., Ávila, A., Matías‐Guiu, J. A., Pytel, V., & Hernández-Fernändez, A. (2021) · Speech Communication · doi:10.1016/j.specom.2021.11.009 · Cited by 47 · see citing works
Zolnoori, M., Zolnour, A., & Topaz, M. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102624 · Cited by 41 · see citing works
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 38 · see citing works
Al-Hameed, S., Benaissa, M., Christensen, H., Mirheidari, B., Blackburn, D., & Reuber, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217388 · Cited by 37 · see citing works
Voice biomarkers as indicators of cognitive changes in middle and later adulthood
Mahon, E., & Lachman, M. E. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.06.010 · Cited by 37 · see citing works
García‐Gutiérrez, F., Marquié, M., Muñoz, N., Alegret, M., Cano, A. C., de Rojas, I., García‐González, P., Olivé, C., Puerta, R., Orellana, A., et al. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1221401 · Cited by 39 · see citing works
Speech-based detection of multi-class Alzheimer’s disease classification using machine learning
Tripathi, T., & Kumar, R. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-023-00475-9 · Cited by 36 · see citing works
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 35 · see citing works
The paradigm and future value of the metaverse for the intervention of cognitive decline
Zhou, H., Gao, J.-Y., & Chen, Y. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1016680 · Cited by 35 · see citing works
Qiao, Y., Xie, X., Lin, G., Zou, Y., Chen, S., Ren, R., & Wang, G. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191056 · Cited by 34 · see citing works
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 37 · see citing works
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 · see citing works
Automatic Detection of Cognitive Impairments through Acoustic Analysis of Speech
Nagumo, R., Zhang, Y., Ogawa, Y., Hosokawa, M., Abe, K., Ukeda, T., Sumi, S., Kurita, S., Nakakubo, S., Lee, S., et al. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666200213094513 · Cited by 33 · see citing works
Gutz, S. E., Stipancic, K. L., Yunusova, Y., Berry, J. D., & Green, J. R. (2022) · Journal of Speech Language and Hearing Research · doi:10.1044/2022_jslhr-21-00589 · Cited by 33 · see citing works
Biosensors toward behavior detection in diagnosis of alzheimer’s disease
Sun, X., Sun, X., Wang, Q., Wang, X., Feng, L., Yang, Y., Jing, Y., Yang, C., & Zhang, S. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.1031833 · Cited by 33 · see citing works
Technology as Infrastructure for Dehumanization:
Oviatt, S. L. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3482855 · Cited by 33 · see citing works
Yamada, Y., Shinkawa, K., Nemoto, M., Nemoto, K., & Arai, T. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101514 · Cited by 31 · see citing works
Alkenani, A. H., Li, Y., Xu, Y., & Zhang, Q. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103803 · Cited by 29 · see citing works
Advances in Multimodal Behavioral Analytics for Early Dementia Diagnosis: A Review
Guruge, C. J. P., Oviatt, S. L., Haghighi, P. D., & Pritchard, E. (2021) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3462244.3479933 · Cited by 30 · see citing works
Speech patterns during memory recall relates to early tau burden across adulthood
Young, C. B., Smith, V., Karjadi, C., Grogan, S., Ang, T. F. A., Insel, P. S., Henderson, V. W., Sumner, M., Poston, K. L., Au, R., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13731 · Cited by 27 · see citing works
Vincze, V., Szatlóczki, G., Tóth, L., Gosztolya, G., Pákáski, M., Hoffmann, I., & Kálmán, J. (2020) · Clinical Linguistics & Phonetics · doi:10.1080/02699206.2020.1827043 · Cited by 28 · see citing works
Self-citationAutomated assessment of speech production and prediction of MCI in older adults
Sanborn, V. E., Ostrand, R., Ciesla, J. A., & Gunstad, J. (2020) · Applied Neuropsychology Adult · doi:10.1080/23279095.2020.1864733 · Cited by 27 · see citing works
Wang, H.-L., Tang, R., Ren, R., Dammer, E. B., Guo, Q., Peng, G., Cui, H., Zhang, Y.-M., Wang, J., Xie, X., et al. (2022) · BMC Medicine · doi:10.1186/s12916-022-02584-x · Cited by 28 · see citing works
Technical Challenges for Smooth Interaction With Seniors With Dementia: Lessons From Humanitude™
Sumioka, H., Shiomi, M., 本田, 美., & Nakazawa, A. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.650906 · Cited by 25 · see citing works
AI-Atroshi, C., Beulah, J. R., Singamaneni, K. K., Cyril, C. P. D., Neelakandan, S., & Velmurugan, S. (2022) · International Journal of Healthcare Management · doi:10.1080/20479700.2022.2097764 · Cited by 26 · see citing works
RetractedAn Evaluation on Information Composition in Dementia Detection Based on Speech
Zheng, C., Bouazizi, M., & Ohtsuki, T. O. (2022) · IEEE Access · doi:10.1109/access.2022.3203068 · Cited by 25 · see citing works
The Internet-Based Cognitive Assessment Tool: System Design and Feasibility Study
Hafiz, P., Miskowiak, K. W., Kessing, L. V., Jespersen, A. E., Obenhausen, K., Gulyas, L., Żukowska, K., & Bardram, J. E. (2019) · JMIR Formative Research · doi:10.2196/13898 · Cited by 25 · see citing works
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 24 · see citing works
Identification of Mild Cognitive Impairment Among Chinese Based on Multiple Spoken Tasks
Wang, T., Hong, Y., Wang, Q., Su, R., Ng, M. L., Xu, J. L., Wang, L., & Yan, N. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201387 · Cited by 23 · see citing works
Nahar, J. K., & López-Jiménez, F. A. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2021.11.006 · Cited by 23 · see citing works
Jonell, P., Moëll, B., Håkansson, K., Henter, G. E., Kucherenko, T., Mikheeva, O., Hagman, G., Holleman, J., Kivipelto, M., Kjellström, H., et al. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.642633 · Cited by 23 · see citing works
Parsapoor, M., Alam, M. R., & Mihailidis, A. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02122-6 · Cited by 24 · see citing works
Wu, R., Li, A., Xue, C., Chai, J., Qiang, Y., Zhao, J., & Wang, L. (2023) · Brain Sciences · doi:10.3390/brainsci13081222 · Cited by 22 · see citing works
Bloom, P. P., Fisher, C., Tedesco, N., Kamdar, N. S., Garrido-Trevino, L., Robin, J., Asrani, S. K., & Lok, A. S.-F. (2024) · Hepatology · doi:10.1097/hep.0000000000001086 · Cited by 21 · see citing works
Screening for early Alzheimer’s disease: enhancing diagnosis with linguistic features and biomarkers
Chou, C., Chang, C.-T., Chang, Y., Lee, C., Chuang, Y., Chiu, Y., Liang, W.-L., Fan, Y.-M., & Liu, Y. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1451326 · Cited by 21 · see citing works
Looze, C. D., Dehsarvi, A., Crosby, L., Vourdanou, A., Coen, R. F., LAWLOR, B. A., & Reilly, R. B. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.637404 · Cited by 21 · see citing works
A rapid, non-invasive method for fatigue detection based on voice information
Gao, X., Ma, K., Yang, H., Wang, K., Fu, B., Zhu, Y., She, X., & Cui, B. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.994001 · Cited by 21 · see citing works
Al-Hammadi, M., Fleyeh, H. A., Åberg, A. C., Halvorsen, K., & Thomas, I. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231459 · Cited by 23 · see citing works
Ding, H., Lister, A., Karjadi, C., Au, R., Lin, H., Bischoff, B., & Hwang, P. H. (2024) · JMIR Aging · doi:10.2196/55126 · Cited by 23 · see citing works
Ambrosini, E., Giangregorio, C., Lomurno, E., Moccia, S., Milis, M., Loizou, C. P., Azzolino, D., Cesari, M., Gala, M. C., de Isla, C. G., et al. (2024) · JMIR Aging · doi:10.2196/50537 · Cited by 20 · see citing works
Jafari, Z., Andrew, M. K., & Rockwood, K. J. (2025) · Age and Ageing · doi:10.1093/ageing/afaf316 · Cited by 20 · see citing works
A novel speech analysis algorithm to detect cognitive impairment in a Spanish population
Kaser, A. N., Lacritz, L. H., Winiarski, H. R., Gabirondo, P., Schaffert, J., Coca, A. J., Jiménez-Raboso, J., Rojo, T., Zaldua, C., Honorato, I., et al. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1342907 · Cited by 17 · see citing works
Svindt, V., Bóna, J., & Hoffmann, I. (2019) · Clinical Linguistics & Phonetics · doi:10.1080/02699206.2019.1645885 · Cited by 18 · see citing works
Self-citationGosztolya, G., Balogh, R., Imre, N., Egas-López, J. V., Hoffmann, I., Vincze, V., Tóth, L., Devanand, D. P., Pákáski, M., & Kálmán, J. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101215 · Cited by 17 · see citing works
Self-citationDeep Neural Network Embeddings for the Estimation of the Degree of Sleepiness
Egas-López, J. V., & Gosztolya, G. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413589 · Cited by 17 · see citing works
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 17 · see citing works
Silva, U. D., Madanian, S., Olsen, S., Templeton, J. M., Poellabauer, C., Schneider, S. L., Narayanan, A., & Rubaiat, R. (2025) · Journal of Medical Internet Research · doi:10.2196/63004 · Cited by 18 · see citing works
Larsen, E., Murton, O. M., Song, X., Joachim, D., Watts, D., Kapczinski, F. P., Venesky, L. G., & Hurowitz, G. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1342835 · Cited by 17 · see citing works
Identifying Schizophrenia Based on Temporal Parameters in Spontaneous Speech
Gosztolya, G., Bagi, A., Szalóki, S., Szendi, I., & Hoffmann, I. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1079 · Cited by 17 · see citing works
Self-citationChien, Y.-W., Hong, S.-Y., Cheah, W.-T., Fu, L.-C., & Chang, Y.-L. (2018) · 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc.2018.00557 · Cited by 15 · see citing works
AI‐based assessments of speech and language impairments in dementia
Parsapoor, M. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13395 · Cited by 15 · see citing works
Asgari, M., Gale, R. C., Wild, K. V., Dodge, H. H., & Kaye, J. A. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666201008110854 · Cited by 14 · see citing works
Deep Learning Based Gait Analysis for Contactless Dementia Detection System from Video Camera
Zhang, Z., Jiang, Y., Cao, X., Yang, X., Zhu, C., Li, Y., & Liu, Y. (2021) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas51556.2021.9401596 · Cited by 15 · see citing works
Yoshii, K., Kimura, D., Kosugi, A., Shinkawa, K., Takase, T., Kobayashi, M., Yamada, Y., Nemoto, M., Watanabe, R., Ota, M., et al. (2022) · JMIR Formative Research · doi:10.2196/42792 · Cited by 14 · see citing works
Martínez‐Nicolás, I., Martínez‐Sánchez, F., Ivanova, O., & Meilán, J. J. G. (2023) · Scientific Reports · doi:10.1038/s41598-023-36804-y · Cited by 14 · see citing works
Clinical classification of memory and cognitive impairment with multimodal digital biomarkers
Banks, R., Higgins, C., Greene, B. R., Jannati, A., Gomes‐Osman, J., Tobyne, S. M., Bates, D. W., & Pascual‐Leone, Á. (2024) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12557 · Cited by 14 · see citing works
Zolnoori, M., Zolnour, A., Vergez, S., Sridharan, S., Spens, I., Topaz, M., Noble, J. M., Henry, S. B., Hirschberg, J., Bowles, K. H., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae300 · Cited by 14 · see citing works
- Authors
- Toth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatloczki, G., Banreti, Z., Pakaski, M., & Kalman, J.
- Affiliations
- MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
- Linguistics Department, University of Szeged, Szeged, Hungary
- MTA-SZTE Research Group on Artificial Intelligence (HU)
- Department of Psychiatry, University of Szeged, Szeged, Hungary
- Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary
- ORCID
- Vincze, Veronika
- Venue
- Current Alzheimer Research
- Publisher
- Bentham Science Publishers Ltd.
- Published
- 2018-01-03
- Volume
- 15
- Issue
- 2
- Pages
- 130-138
- ISSN
- 1567-2050 (ISSN-L)
- PMC
- PMC5815089
- OpenAlex
- W2770604845
- Cited by
- 290
- References
- 49
- Citations by year
- 2026: 39 · 2025: 37 · 2024: 42 · 2023: 46 · 2022: 31 · 2021: 44 · 2020: 26 · 2019: 19 · 2018: 3 · 2017: 2
- FWCI
- 11.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Current Alzheimer Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- SZTE Publicatio Repozitórium (University of Szeged) (acceptedVersion, other-oa)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- Funders
- Magyar Tudományos Akadémia
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Keywords
- mild cognitive impairment, Alzheimer's disease, spontaneous speech, speech tempo, acoustic features, speech hesitations, pauses, neuropsychological screening, automated detection, memory retrieval
- MeSH terms
- Machine Learning, Aged, Aged, 80 and over, Diagnosis, Computer-Assisted, Female, Humans, Male, Memory, Middle Aged, Neuropsychological Tests, Pattern Recognition, Automated, ROC Curve, Speech, Speech Production Measurement, Models, Statistical, Internet, Speech Recognition Software, Cognitive Dysfunction
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
