{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T12:01:26Z","timestamp":1781092886594,"version":"3.54.1"},"reference-count":47,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":40,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["alz-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Alz &amp; Dem Diag Ass &amp; Dis Mo"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>INTRODUCTION<\/jats:title>\n                    <jats:p>Early detection of mild cognitive impairment (MCI) is critical for intervention and dementia prevention. Interpretable linguistic features may offer transparent, cross\u2010linguistic markers yet remain underexplored in bilingual dataset\u00a0contexts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>Using the TAUKADIAL Challenge dataset, which includes English and Chinese picture descriptions, we extracted 93 language\u2010specific linguistic features with the Efficient Linguistic Feature Extraction for Natural Language Datasets (ELFEN) package and 141 language\u2010agnostic linguistic features using the Comprehensive Handcrafted Linguistic Features (LFTK) toolkit. One\u2010way ANOVA and Tukey's Honestly Significant Difference tests assessed associations with diagnosis, task, and\u00a0language.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Seven ELFEN and 33 LFTK features showed significant differences between diagnostic groups. MCI speech exhibited reduced lexical diversity, fewer pronouns, greater use of numerals and participles, and longer sentences across both languages. Task\u2010 and language\u2010based analyses revealed structural and lexical variability, with greater variability in Chinese\u00a0responses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>DISCUSSION<\/jats:title>\n                    <jats:p>These findings identify statistically significant, interpretable linguistic features associated with MCI, establishing a cross\u2010linguistic foundation for developing transparent, multilingual tools for early cognitive\u00a0assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Highlights<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>Nintey\u2010three language\u2010specific and 141 language\u2010agnostic features are analyzed from bilingual speech.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Seven ELFEN and 33 LFTK features were identified as significantly linked to MCI diagnosis.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>MCI speakers used fewer pronouns, showed lower lexical diversity, and produced longer sentences.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Findings reveal consistent cross\u2010linguistic markers in English and Chinese picture descriptions.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The study offers an interpretable, statistically validated foundation for multilingual MCI screening tools.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dad2.70246","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T10:24:24Z","timestamp":1770719064000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Statistical analysis of interpretable linguistic features for MCI detection in bilingual speech"],"prefix":"10.1002","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3823-2619","authenticated-orcid":false,"given":"Arezo","family":"Shakeri","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mina","family":"Farmanbar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/02692163211025457"},{"key":"e_1_2_11_3_1","first-page":"509","article-title":"Evaluating combinations of diagnostic tests to discriminate different dementia types","volume":"10","author":"Bruun M","year":"2018","journal-title":"Alzheimer's Dement"},{"key":"e_1_2_11_4_1","first-page":"56","article-title":"Knowledge of semantic features in mild cognitive impairment","volume":"38","author":"Taler V","year":"2016","journal-title":"J Neurol"},{"key":"e_1_2_11_5_1","doi-asserted-by":"crossref","unstructured":"LuzS HaiderF de laFuenteS FrommD MacWhinneyB.Alzheimer's dementia recognition through spontaneous speech: the ADReSS Challenge. 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