Hostname: page-component-68c7f8b79f-qcl88 Total loading time: 0 Render date: 2025-12-21T16:29:10.717Z Has data issue: false hasContentIssue false
  • English
  • Français

Speech pause and speech rate for evaluating Alzheimer’s and mild cognitive impairment: A meta-analysis

Published online by Cambridge University Press:  11 December 2025

Alex S. Cohen ,
Ross Divers Open the ORCID record for Ross Divers [Opens in a new window] ,
Matthew Calamia Open the ORCID record for Matthew Calamia [Opens in a new window] ,
Michael Masucci ,
Ole Edvard Granrud Open the ORCID record for Ole Edvard Granrud [Opens in a new window] ,
Aubree Corporandy ,
Kiara Kamil Warren ,
Brita Elvevåg ,
Chelsea Chandler  and
Catherine Diaz-Asper
Alex S. Cohen*
Affiliation:
Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
Ross Divers
Affiliation:
Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
Matthew Calamia
Affiliation:
Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, USA
Michael Masucci
Affiliation:
Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
Ole Edvard Granrud
Affiliation:
Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
Aubree Corporandy
Affiliation:
Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
Kiara Kamil Warren
Affiliation:
Department of Psychology, Louisiana State University, Baton Rouge, LA, USA
Brita Elvevåg
Affiliation:
Norwegian Center for Clinical Artificial Intelligence, University Hospital of North Norway, Tromsø, Norway Department of Clinical Medicine, University of Tromsø—The Arctic University of Norway, Tromsø, Norway
Chelsea Chandler
Affiliation:
Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA
Catherine Diaz-Asper
Affiliation:
Department of Psychology, Marymount University, Arlington, VA, USA
*
Corresponding author: Alex S. Cohen; Email: acohen@lsu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

Evaluating pauses in natural speech is a promising strategy for improving reliability, validity, and efficiency in assessing cognitive functions in people with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD).

Method:

We conducted a quantitative meta-analysis of studies employing automated pause analysis. We included measures of speaking rate for comparison.

Results:

We identified 13 studies evaluating pause measures and 8 studies of speaking rate in people with MCI (n’s = 276 & 109, respectively) and AD (n’s = 170 & 81, respectively) and healthy aged controls (n’s = 492 & 231, respectively). Studies evaluated speech across various tasks, including standard neuropsychological, reading, and free/conversational tasks. People with AD and MCI showed longer pauses than controls at approximately 1.20 and 0.62 standard deviations, respectively, though there was substantial heterogeneity across studies. A more modest effect, of 0.66 and 0.27 SDs, was observed between these groups in speech rate. The largest effects were observed for standardized memory tasks.

Conclusions:

Of the many ways that speech can be objectified, pauses appear particularly important for understanding cognition in AD. Pause analysis has the benefit of being face valid, interpretable in ratio format as a reaction time, tied to known socio-cognitive functions, and relatively easy to measure, compute, and interpret. Automation of speech analysis can greatly expand the assessment of AD and potentially improve early identification of one of the most devastating and costly diseases affecting humans.

Keywords

Dementialanguageacousticsspeechbiomarkerneuropsychological

Information

Type
Research Article
Information
Journal of the International Neuropsychological Society , First View , pp. 1 - 8
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Neuropsychological Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Angelopoulou, G., Kasselimis, D., Goutsos, D., & Potagas, C. (2024). A methodological approach to quantifying silent pauses, speech rate, and articulation rate across distinct narrative tasks: Introducing the connected speech analysis protocol (CSAP). Brain Sciences, 14(5), 466. Article 5.10.3390/brainsci14050466CrossRefGoogle ScholarPubMed
Balduzzi, S., Rücker, G., & Schwarzer, G. (2019). How to perform a meta-analysis with R: A practical tutorial. Evidence-Based Mental Health, 22(4), 153–160.10.1136/ebmental-2019-300117CrossRefGoogle Scholar
Calamia, M., Markon, K., & Tranel, D. (2013). The robust reliability of neuropsychological measures: Meta-analyses of test–retest correlations. Clinical Neuropsychologist, 27(7), 1077–1105.10.1080/13854046.2013.809795CrossRefGoogle ScholarPubMed
Chandler, C., Diaz-Asper, C., Turner, R. S., Reynolds, B., & Elvevåg, B. (2023). An explainable machine learning model of cognitive decline derived from speech. Alzheimer’s & Dementia (Amsterdam, Netherlands), 15(4), e12516.Google ScholarPubMed
Chandler, C., Foltz, P. W., & Elvevåg, B. (2020). Using machine learning in psychiatry: The need to establish a framework that nurtures trustworthiness. Schizophrenia Bulletin, 46(1), 11–14.Google ScholarPubMed
Cohen, A. S., Dinzeo, T. J., Donovan, N. J., Brown, C. E., & Morrison, S. C. (2015). Vocal acoustic analysis as a biometric indicator of information processing: Implications for neurological and psychiatric disorders. Psychiatry Research, 226(1), 235–241.10.1016/j.psychres.2014.12.054CrossRefGoogle ScholarPubMed
Cohen, A. S., & Elvevåg, B. (2014). Automated computerized analysis of speech in psychiatric disorders. Current Opinion in Psychiatry, 27(3), 203–209.10.1097/YCO.0000000000000056CrossRefGoogle ScholarPubMed
Cohen, A. S., McGovern, J. E., Dinzeo, T. J., & Covington, M. A. (2014). Speech deficits in serious mental illness: A cognitive resource issue? Schizophrenia Research, 160(1-3), 173–179.10.1016/j.schres.2014.10.032CrossRefGoogle ScholarPubMed
Cohen, A. S., Mitchell, K. R., & Elvevåg, B. (2014). What do we really know about blunted vocal affect and alogia? A meta-analysis of objective assessments. Schizophrenia Research, 159(2-3), 533–538.10.1016/j.schres.2014.09.013CrossRefGoogle ScholarPubMed
Cohen, A. S., Morrison, S. C., Brown, L. A., & Minor, K. S. (2012). Towards a cognitive resource limitations model of diminished expression in schizotypy. Journal of Abnormal Psychology, 121(1), 109–118.10.1037/a0023599CrossRefGoogle ScholarPubMed
Cohen, A. S., Rodriguez, Z., Opler, M., Kirkpatrick, B., Milanovic, S., Piacentino, D., Szabo, S. T., Tomioka, S., Ogirala, A., Koblan, K. S., Siegel, J. S., & Hopkins, S. (2024). Evaluating speech latencies during structured psychiatric interviews as an automated objective measure of psychomotor slowing. Psychiatry Research, 340, 116104.10.1016/j.psychres.2024.116104CrossRefGoogle Scholar
Cummins, N., Epps, J., Breakspear, M., & Goecke, R. (2011). An investigation of depressed speech detection: Features and normalization. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH , pp. 2997–3000.10.21437/Interspeech.2011-750CrossRefGoogle Scholar
De La Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020). Artificial intelligence, speech, and language processing approaches to monitoring Alzheimer’s disease: A systematic review. Journal of Alzheimer’s Disease, 78(4), 1547–1574.10.3233/JAD-200888CrossRefGoogle ScholarPubMed
Diaz-Asper, C., Chandler, C., Elvevåg, B., & Thomas, K. (2024). Cognitive screening for mild cognitive impairment: Clinician perspectives on current practices and future directions. Journal of Alzheimer’s Disease: JAD, 99(3), 869–876.10.3233/JAD-240293CrossRefGoogle ScholarPubMed
Diaz-Asper, C., Chandler, C., Turner, R. S., Reynolds, B., & Elvevåg, B. (2022). Increasing access to cognitive screening in the elderly: Applying natural language processing methods to speech collected over the telephone. Cortex; A Journal Devoted to the Study of the Nervous System and Behavior, 156, 26–38.10.1016/j.cortex.2022.08.005CrossRefGoogle ScholarPubMed
Diaz-Asper, C., Hauglid, M. K., Chandler, C., Cohen, A. S., Foltz, P. W., & Elvevåg, B. (2024). A framework for language technologies in behavioral research and clinical applications: Ethical challenges, implications, and solutions. American Psychologist, 79(1), 79.10.1037/amp0001195CrossRefGoogle Scholar
Divers, R., Cohen, A. S., Elvevåg, B., Chandler, C., Turner, R. S., Reynolds, B., & Diaz-Asper, C. (2025). Speech production as an artificial intelligence-based ‘process’ measure of cognition sensitive to mild cognitive impairment and Alzheimer’s disease. The Clinical Neuropsychologist, 1–16. https://doi.org/10.1080/13854046.2025.2519827 CrossRefGoogle Scholar
Foltz, P. W., Chandler, C., Diaz-Asper, C., Cohen, A. S., Rodriguez, Z., Holmlund, T. B., & Elvevåg, B. (2023). Reflections on the nature of measurement in language-based automated assessments of patients’ mental state and cognitive function. Schizophrenia Research, 259, 127–139.10.1016/j.schres.2022.07.011CrossRefGoogle ScholarPubMed
Han, S. D., Nguyen, C. P., Stricker, N. H., & Nation, D. A. (2017). Detectable neuropsychological differences in early preclinical Alzheimer’s disease: A meta-analysis. Neuropsychology Review, 27(4), 305–325.10.1007/s11065-017-9366-0CrossRefGoogle ScholarPubMed
Hartung, J., & Knapp, G. (2001a). A refined method for the meta-analysis of controlled clinical trials with binary outcome. Statistics in Medicine, 20(24), 3875–3889.10.1002/sim.1009CrossRefGoogle ScholarPubMed
Hartung, J., & Knapp, G. (2001b). On tests of the overall treatment effect in meta-analysis with normally distributed responses. Statistics in Medicine, 20(12), 1771–1782.10.1002/sim.791CrossRefGoogle ScholarPubMed
Hastie, T., Tibshirani, R., & Tibshirani, R. J. (2017). Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv preprint arXiv: 1707.08692.Google Scholar
Higgins, J. P., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta-analysis. Statistics in Medicine, 21(11), 1539–1558.10.1002/sim.1186CrossRefGoogle ScholarPubMed
Hitczenko, K., Segal, Y., Keshet, J., Goldrick, M., & Mittal, V. A. (2023). Speech characteristics yield important clues about motor function: Speech variability in individuals at clinical high-risk for psychosis. Schizophrenia, 9(1), Article–1.10.1038/s41537-023-00382-9CrossRefGoogle ScholarPubMed
Jedenius, E., Wimo, A., Strömqvist, J., Jönsson, L., & Andreasen, N. (2010). The cost of diagnosing dementia in a community setting. International Journal of Geriatric Psychiatry, 25(5), 476–482.10.1002/gps.2365CrossRefGoogle Scholar
Kim, K. W., Na, S.-H., Chung, Y.-C., & Shin, B.-S. (2021). A comparison of speech features between mild cognitive impairment and healthy aging groups. Dementia and Neurocognitive Disorders, 20(4), 52–61.10.12779/dnd.2021.20.4.52CrossRefGoogle ScholarPubMed
Lanzi, A. M., Saylor, A. K., Fromm, D., Liu, H., MacWhinney, B., & Cohen, M. L. (2023). DementiaBank: Theoretical rationale, protocol, and illustrative analyses. American Journal of Speech–Language Pathology, 32(2), 426–438.10.1044/2022_AJSLP-22-00281CrossRefGoogle ScholarPubMed
Michalowsky, B., Flessa, S., Hertel, J., Goetz, O., Hoffmann, W., Teipel, S., & Kilimann, I. (2017). Cost of diagnosing dementia in a German memory clinic. Alzheimer’s Research & Therapy, 9(1), 65.10.1186/s13195-017-0290-6CrossRefGoogle Scholar
Oldfield, R. C., & Wingfield, A. (1965). Response latencies in naming objects. Quarterly Journal of Experimental Psychology, 17(4), 273–281.10.1080/17470216508416445CrossRefGoogle ScholarPubMed
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., …Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.10.1136/bmj.n71CrossRefGoogle ScholarPubMed
Parola, A., Simonsen, A., Bliksted, V., & Fusaroli, R. (2020). Voice patterns in schizophrenia: A systematic review and Bayesian meta-analysis. Schizophrenia Research, 216, 24–40.10.1016/j.schres.2019.11.031CrossRefGoogle ScholarPubMed
Saeedi, S., Hetjens, S., Grimm, M. O. W., & Latoszek, B. B. V. (2024). Acoustic speech analysis in Alzheimer’s disease: A systematic review and meta-analysis. The Journal of Prevention of Alzheimer’s Disease, 11(6), 1789–1797.10.14283/jpad.2024.132CrossRefGoogle ScholarPubMed
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.10.18637/jss.v036.i03CrossRefGoogle Scholar
Walker, G., Pevy, N., O’Malley, R., Mirheidari, B., Reuber, M., Christensen, H., & Blackburn, D. J. (2023). Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls. Clinical Linguistics & Phonetics, 38(9), 880–901.10.1080/02699206.2023.2254458CrossRefGoogle Scholar
Wang, C., Lee, C., & Shin, H. (2023). Digital therapeutics from bench to bedside. Npj Digital Medicine, 6(1), 38.10.1038/s41746-023-00777-zCrossRefGoogle ScholarPubMed
Wells, B., & Macfarlane, S. (1998). Prosody as an interactional resource: Turn-projection and overlap. Language and Speech, 41(3-4), 265–294.10.1177/002383099804100403CrossRefGoogle Scholar
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.10.1177/1745691617693393CrossRefGoogle ScholarPubMed
Yuan, J., Cai, X., Bian, Y., Ye, Z., & Church, K. (2021). Pauses for detection of Alzheimer’s disease. Frontiers in Computer Science, 2, 624488.10.3389/fcomp.2020.624488CrossRefGoogle Scholar
Supplementary material: File

Cohen et al. supplementary material

Cohen et al. supplementary material
Download Cohen et al. supplementary material(File)
File 127.1 KB