{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:12:07Z","timestamp":1788394327193,"version":"build-2803163510"},"reference-count":72,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01 AG071514"],"award-info":[{"award-number":["R01 AG071514"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01 AG069765"],"award-info":[{"award-number":["R01 AG069765"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01 NS101483"],"award-info":[{"award-number":["R01 NS101483"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000957","name":"Alzheimer's Association","doi-asserted-by":"publisher","award":["AARF-22-923592"],"award-info":[{"award-number":["AARF-22-923592"]}],"id":[{"id":"10.13039\/100000957","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007049","name":"Evelyn F. McKnight Brain Research Foundation","doi-asserted-by":"publisher","award":["FP00006751"],"award-info":[{"award-number":["FP00006751"]}],"id":[{"id":"10.13039\/100007049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019591","name":"Harry T. Mangurian, Jr. Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019591","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD) is characterized by progressive cognitive decline, including impairments in speech production and fluency. Mild cognitive impairment (MCI), a prodrome of AD, has also been linked with changes in speech behavior but to a more subtle degree.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to investigate whether speech behavior immediately following both filled and unfilled pauses (post-pause speech behavior) differs between individuals with MCI and healthy controls (HCs), and how these differences are influenced by the cognitive demands of various speech tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Transcribed speech samples were analyzed from both groups across different tasks, including immediate and delayed narrative recall, picture descriptions, and free responses. Key metrics including lexical and syntactic complexity, lexical frequency and diversity, and part of speech usage, both overall and post-pause, were examined.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Significant differences in pause usage were observed between groups, with a higher incidence and longer latencies following these pauses in the MCI group. Lexical frequency following filled pauses was higher among MCI participants in the free response task but not in other tasks, potentially due to the relative cognitive load of the tasks. The immediate recall task was most useful at differentiating between groups. Predictive analyses utilizing random forest classifiers demonstrated high specificity in using speech behavior metrics to differentiate between MCI and HCs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Speech behavior following pauses differs between MCI participants and healthy controls, with these differences being influenced by the cognitive demands of the speech tasks. These post-pause speech metrics can be easily integrated into existing speech analysis paradigms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877241291239","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T01:55:01Z","timestamp":1732499701000},"page":"815-829","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["High frequency post-pause word choices and task-dependent speech behavior characterize connected speech in individuals with mild cognitive impairment"],"prefix":"10.1177","volume":"102","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-5502","authenticated-orcid":false,"given":"Michael J","family":"Kleiman","sequence":"first","affiliation":[{"name":"Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Boca Raton, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James E","family":"Galvin","sequence":"additional","affiliation":[{"name":"Comprehensive Center for Brain Health, Department of Neurology, University of Miami Miller School of Medicine, Boca Raton, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10072-005-0467-9"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awt269"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1080\/13803395.2016.1179266"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-150520"},{"key":"e_1_3_5_6_2","article-title":"Speech and language characteristics differentiate Alzheimer\u2019s disease and dementia with Lewy bodies","volume":"14","author":"Yamada Y","year":"2022","unstructured":"Yamada Y, Shinkawa K, Nemoto M, et al. Speech and language characteristics differentiate Alzheimer\u2019s disease and dementia with Lewy bodies. Alzheimers Dement (Amst) 2022; 14: e12364.","journal-title":"Alzheimers Dement (Amst)"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2020.624488"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2017.00269"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1212\/CON.0000000000000313"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0236009"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2017.00437"},{"key":"e_1_3_5_12_2","volume-title":"BDAE: The Boston diagnostic aphasia examination","author":"Goodglass H","year":"2001","unstructured":"Goodglass H, Kaplan E, Weintraub S. BDAE: The Boston diagnostic aphasia examination. Philadelphia: Lippincott Williams & Wilkins, 2001."},{"key":"e_1_3_5_13_2","volume-title":"Wechsler memory scale-revised","author":"Wechsler D","year":"1987","unstructured":"Wechsler D. Wechsler memory scale-revised. New York: The Psychological Corporation, 1987."},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/0197-4580(95)02002-0"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2018.00369"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.3115\/1572392.1572394"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcomp.2021.634360"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1075\/ml.10.2.05tal"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2019.00205"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2012.12.006"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2013.02.013"},{"key":"e_1_3_5_22_2","volume-title":"Psycholinguistics: Experiments in spontaneous speech","author":"Goldman-Eisler F","year":"1968","unstructured":"Goldman-Eisler F. Psycholinguistics: Experiments in spontaneous speech. London & New York: Academic Press, 1968."},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.1037\/xlm0001103"},{"key":"e_1_3_5_24_2","first-page":"1","volume-title":"Proceedings of the workshop on extra-propositional aspects of meaning in computational linguistics","author":"Womack K","year":"2012","unstructured":"Womack K, McCoy W, Ovesdotter Alm C, et al. Disfluencies as extra-propositional indicators of cognitive processing. In: Morante R, Sporleder C (eds) Proceedings of the workshop on extra-propositional aspects of meaning in computational linguistics. Jeju, Republic of Korea: Association for Computational Linguistics, 2012, pp.1\u20139."},{"key":"e_1_3_5_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0010-0277(02)00017-3"},{"key":"e_1_3_5_26_2","doi-asserted-by":"publisher","DOI":"10.1017\/S0025100301001128"},{"key":"e_1_3_5_27_2","first-page":"130","article-title":"A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech","volume":"14","author":"Toth L","year":"2017","unstructured":"Toth L, Hoffmann I, Gosztolya G, et al. A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech. Curr Alzheimer Res 2017; 14: 130\u2013138.","journal-title":"Curr Alzheimer Res"},{"key":"e_1_3_5_28_2","doi-asserted-by":"publisher","DOI":"10.1080\/02699206.2020.1827043"},{"key":"e_1_3_5_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2022.101377"},{"key":"e_1_3_5_30_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-201387"},{"key":"e_1_3_5_31_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0293634"},{"key":"e_1_3_5_32_2","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.43.11.2412-a"},{"key":"e_1_3_5_33_2","doi-asserted-by":"publisher","DOI":"10.1097\/WAD.0000000000000223"},{"key":"e_1_3_5_34_2","doi-asserted-by":"publisher","DOI":"10.1590\/1980-57642015DN92000008"},{"key":"e_1_3_5_35_2","doi-asserted-by":"publisher","DOI":"10.1044\/2018_AJSLP-17-0131"},{"key":"e_1_3_5_36_2","doi-asserted-by":"publisher","DOI":"10.1080\/02687038.2015.1081138"},{"key":"e_1_3_5_37_2","doi-asserted-by":"publisher","DOI":"10.1044\/2022_AJSLP-22-00291"},{"key":"e_1_3_5_38_2","unstructured":"Radford A Kim JW Xu T et al. Robust speech recognition via large-scale weak supervision. arXiv 2022. doi: https:\/\/doi.org\/10.48550\/arXiv.2212.04356."},{"key":"e_1_3_5_39_2","unstructured":"Robert J Webbie M Jean-philippe S et al. PyDub https:\/\/github.com\/jiaaro\/pydub (2023 accessed 12 July 2023)."},{"key":"e_1_3_5_40_2","volume-title":"PySOX: Leveraging the audio signal processing power of SOX in Python","author":"Bittner R","year":"2016","unstructured":"Bittner R, Humphrey EJ, Bello J. PySOX: Leveraging the audio signal processing power of SOX in Python. New York: Proceedings of the 17th International Society for Music Information Retrieval Conference Late Breaking and Demo Papers, 2016."},{"key":"e_1_3_5_41_2","article-title":"spaCy: Industrial-strength natural language processing in Python","author":"Honnibal M","year":"2020","unstructured":"Honnibal M, Montani I, Van Landeghem S, et al. spaCy: Industrial-strength natural language processing in Python. Zenodo 2020. doi: https:\/\/doi.org\/10.5281\/zenodo.1212303","journal-title":"Zenodo"},{"key":"e_1_3_5_42_2","article-title":"rspeer\/wordfreq: v3.","author":"Speer R","year":"2022","unstructured":"Speer R. rspeer\/wordfreq: v3.0. Zenodo 2022. doi: https:\/\/doi.org\/10.5281\/zenodo.7199437.","journal-title":"Zenodo"},{"key":"e_1_3_5_43_2","doi-asserted-by":"publisher","DOI":"10.21105\/joss.05153"},{"key":"e_1_3_5_44_2","doi-asserted-by":"publisher","DOI":"10.1037\/h0076540"},{"key":"e_1_3_5_45_2","doi-asserted-by":"publisher","DOI":"10.3758\/BRM.41.4.977"},{"key":"e_1_3_5_46_2","first-page":"444","article-title":"A model and an hypothesis for language structure","volume":"104","author":"Yngve VH","year":"1960","unstructured":"Yngve VH. A model and an hypothesis for language structure. Proc Am Philos Soc 1960; 104: 444\u2013466.","journal-title":"Proc Am Philos Soc"},{"key":"e_1_3_5_47_2","doi-asserted-by":"publisher","DOI":"10.1080\/09296171003643098"},{"key":"e_1_3_5_48_2","doi-asserted-by":"publisher","DOI":"10.3758\/BRM.42.2.381"},{"key":"e_1_3_5_49_2","doi-asserted-by":"publisher","DOI":"10.1177\/00238309010440020101"},{"key":"e_1_3_5_50_2","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01026"},{"key":"e_1_3_5_51_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1532-5415.2005.53221.x"},{"key":"e_1_3_5_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jagp.2019.01.125"},{"key":"e_1_3_5_53_2","doi-asserted-by":"publisher","DOI":"10.1080\/13854049108403297"},{"key":"e_1_3_5_54_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0242233"},{"key":"e_1_3_5_55_2","article-title":"The Vulnerability Index: a weighted measure of dementia and cognitive impairment risk","volume":"13","author":"Kleiman MJ","year":"2021","unstructured":"Kleiman MJ, Galvin JE. The Vulnerability Index: a weighted measure of dementia and cognitive impairment risk. Alzheimers Dement (Amst) 2021; 13: e12249.","journal-title":"Alzheimers Dement (Amst)"},{"key":"e_1_3_5_56_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-215077"},{"key":"e_1_3_5_57_2","first-page":"3149","volume-title":"Proceedings of the 31st international conference on neural information processing systems","author":"Ke G","year":"2017","unstructured":"Ke G, Meng Q, Finley T, et al. LightGBM: A highly efficient gradient boosting decision tree. In: Proceedings of the 31st international conference on neural information processing systems. Red Hook, NY, USA: Curran Associates, Inc., 2017, pp.3149\u20133157."},{"key":"e_1_3_5_58_2","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa F","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011; 12: 2825\u20132830.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_5_59_2","article-title":"BorutaShap: A wrapper feature selection method which combines the Boruta feature selection algorithm with Shapley values","author":"Keany E","year":"2020","unstructured":"Keany E. BorutaShap: A wrapper feature selection method which combines the Boruta feature selection algorithm with Shapley values. Zenodo 2020. doi: https:\/\/doi.org\/10.5281\/zenodo.4247618.","journal-title":"Zenodo"},{"key":"e_1_3_5_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_5_61_2","doi-asserted-by":"publisher","DOI":"10.1093\/applin\/amx054"},{"key":"e_1_3_5_62_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44566-8_3"},{"key":"e_1_3_5_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-2166(98)00014-9"},{"key":"e_1_3_5_64_2","volume-title":"Fluency and disfluency across languages and language varieties","author":"Degand L","year":"2019","unstructured":"Degand L, Gilquin G, Meurant L, et al. Fluency and disfluency across languages and language varieties. Louvain-la-Neuve: Presses universitaires de Louvain, 2019."},{"key":"e_1_3_5_65_2","doi-asserted-by":"publisher","DOI":"10.1093\/geronb\/59.6.P285"},{"key":"e_1_3_5_66_2","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0655-14.2014"},{"key":"e_1_3_5_67_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22239"},{"key":"e_1_3_5_68_2","volume-title":"Making way in corpus-based interpreting studies","author":"Russo M","year":"2017","unstructured":"Russo M, Bendazzoli C, Defrancq B. Making way in corpus-based interpreting studies. Singapore: Springer, 2017."},{"key":"e_1_3_5_69_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-201377"},{"key":"e_1_3_5_70_2","first-page":"2162","article-title":"Disfluencies and fine-tuning pre-trained language models for detection of Alzheimer\u2019s disease","author":"Yuan J","year":"2020","unstructured":"Yuan J, Bian Y, Cai X, et al. Disfluencies and fine-tuning pre-trained language models for detection of Alzheimer\u2019s disease. Proc Interspeech 2020: 2162\u20132166.","journal-title":"Proc Interspeech"},{"key":"e_1_3_5_71_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2020.07.008"},{"key":"e_1_3_5_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2112351"},{"key":"e_1_3_5_73_2","doi-asserted-by":"publisher","DOI":"10.1080\/13803395.2018.1446513"}],"container-title":["Journal of Alzheimer\u2019s Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/13872877241291239","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/13872877241291239","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/13872877241291239","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T10:09:09Z","timestamp":1777370949000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/13872877241291239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,13]]},"references-count":72,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10.1177\/13872877241291239"],"URL":"https:\/\/doi.org\/10.1177\/13872877241291239","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.02.25.24303329","asserted-by":"object"}]},"ISSN":["1387-2877","1875-8908"],"issn-type":[{"value":"1387-2877","type":"print"},{"value":"1875-8908","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,13]]}}}