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While biomarkers such as neuroimaging and cerebrospinal fluid analyses offer high sensitivity, their limited accessibility hampers widespread screening, especially in underserved settings. Speech-based markers have emerged as promising, noninvasive indicators of cognitive decline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate SpeechDETECT, an end-to-end speech-processing pipeline that captures fine-grained acoustic and temporal markers of cognitive impairment and provides interpretable outputs suitable for large-scale screening.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>SpeechDETECT comprises six modules: (1) noise reduction \/ amplitude normalization; (2) an eight-domain voice-analysis framework (e.g., frequency parameters, speech fluency); (3) 50\u00a0ms segment-level feature extraction; (4) feature visualization; (5) dimensionality reduction \/ selection (Joint Mutual Information Maximization, LassoNet, PCA); and (6) classifier training with SHapley Additive exPlanations (SHAP). Performance was benchmarked against six acoustic toolkits (e.g., GeMAPS) on two English datasets: the DementiaBank Pitt corpus (train\u2009=\u2009166, test\u2009=\u200971) with single cookie-theft picture description task and NIA PREPARE Phase 2 corpus (train\u2009=\u20091 064, test\u2009=\u2009267) with multiple speech tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A Multi-Layer Perceptron trained on PCA-derived SpeechDETECT features achieved an\u00a0F1-score\u2009=\u20090.81% and AUC-ROC\u2009=\u20090.80\u00a0on the Pitt test set, outperforming the best competing toolkit (AUC\u2009=\u20090.76). On the PREPARE test set\u2014comprising\u2009\u2264\u200930\u00a0s recordings from four speech tasks\u2014the same model attained\u00a0F1 \u2248 0.67% and AUC-ROC\u2009=\u20090.70\n                      <jats:bold>,<\/jats:bold>\n                      demonstrating good generalizability. Cumulative-gains analysis showed that screening the top 40% of ranked participants captured\u2009~\u200970% of cognitively-impaired (CI) cases in Pitt and\u2009~\u200963% in PREPARE. SHAP revealed speech-fluency metrics (hesitation rate, pause ratio) and high-frequency formant dynamics as the most discriminative features.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      SpeechDETECT delivers accurate (AUC up to 0.80) and interpretable detection of early cognitive impairment across both structured and multi-task speech settings. Its fully automated, domain-informed approach enables scalable, speech-based screening and provides a foundation for multimodal systems that combine acoustic markers with clinical or biomarker data to further improve diagnostic precision\n                      <jats:italic>.<\/jats:italic>\n                      The SpeechDETECT toolkit is openly available on GitHub at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/SpeechCARE\/SpeechDETECT-Toolkit\" ext-link-type=\"uri\">https:\/\/github.com\/SpeechCARE\/SpeechDETECT-Toolkit<\/jats:ext-link>\n                      for researchers and clinicians.\u00a0A demo tutorial video showing pipeline usage is available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/SpeechCARE\/SpeechDETECT-Toolkit\/blob\/main\/SpeechDETECT.mp4\" ext-link-type=\"uri\">https:\/\/github.com\/SpeechCARE\/SpeechDETECT-Toolkit\/blob\/main\/SpeechDETECT.mp4<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s13755-026-00468-5","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T17:52:09Z","timestamp":1784915529000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SpeechDETECT: an explainable automated speech processing pipeline for early detection of neurological and health changes"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4484-2990","authenticated-orcid":false,"given":"Maryam","family":"Zolnoori","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elyas","family":"Esmaeili","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Naserian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Zolnour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sina","family":"Rashidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahoura","family":"Morovati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Azadmaleki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James M.","family":"Noble","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Margaret V.","family":"McDonald","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"468_CR1","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1212\/01.wnl.0000249117.23318.e1","volume":"67","author":"A Busse","year":"2006","unstructured":"Busse A, Hensel A, G\u00fchne U, Angermeyer MC, Riedel-Heller SG. 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