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Acoustic biomarkers from spontaneous speech—via changes in prosody, fluency, and pause patterns—enable a non-invasive route to earlier diagnosis. Using PREPARE Phase 2 Challenge data (over 1,600 individuals), the study benchmarks open-source foundation speech and language models for classifying cognitive decline stages. Whisper-medium achieves highest speech-based performance, and prosody-driven features further improve text-based classification.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/benchmarking-foundation-models-for-alzheimers-disease-and-related-dementia-detection-from-spontaneous-speech/426854/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/benchmarking-foundation-models-for-alzheimers-disease-and-related-dementia-detection-from-spontaneous-speech/426854.png","ImageObject",300,407,{"name":92,"@type":93},"WPS_1790064749","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-12","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early detection important for Alzheimer’s disease and related dementias (ADRD)?","Question",{"text":112,"@type":113},"Early detection supports timely intervention and care planning for these progressive neurodegenerative conditions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study use spontaneous speech for ADRD detection?",{"text":117,"@type":113},"It extracts acoustic biomarkers from spontaneous speech, including prosody, fluency, and pause patterns, to enable non-invasive diagnosis.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model and features improved performance in the benchmarking results?",{"text":121,"@type":113},"Whisper-medium achieved the highest speech-based performance, and incorporating prosodic features such as pauses improved classification for text-based approaches.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},426854,1790767909,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":24},3985747859154,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","1218 Innovation in Aging, 2025, Vol. 9, No. S2  \nAbstract citation ID: igaf122.4077  \nBENCHMARKING FOUNDATION MODELS FOR ALZHEIMER’S DISEASE AND RELATED DEMENTIA DETECTION FROM SPONTANEOUS SPEECH  \nJingyu Li1, Lingchao Mao1, Hairong Wang2, Zhendong Wang3, Xi Mao4, and Xuelei Ni5, 1. Georgia Institute of Technology, Atlanta, Georgia, United States, 2. The University of Texas at Austin, Austin, Texas, United States, 3. Shandong Mental Health Center, Jinan, Shandong, China (People’s Republic), 4. University of Texas Rio Grande Valley, Brownsville, Texas, United States, 5. Kennesaw State University, Kennesaw, Georgia, United States  \nAlzheimer’s disease and related dementias (ADRD) are progressive neurodegenerative conditions where early detection is critical for timely intervention and care planning. Acoustic  \nInnovation in Aging, 2025, Vol. 9, No. S2 1219  \nbiomarkers—such as changes in prosody, fluency, and pause patterns—can be extracted from spontaneous speech and offera non-invasive avenue for early diagnosis. Foundational speech and language models, which are pre-trained deep learning models, can generate high-dimensional embeddings that capture rich contextual and acoustic information from raw audio or text.  \nUsing data from the PREPARE Phase 2 Challenge, which includes recordings from over 1,600 individuals, we examined the potential of foundation models for ADRD detection. Specifically, we benchmarked a range of open-source speech and language models on their ability to classify participants into different stages of cognitive decline. Among speech-based approaches, the Whisper-medium model achieved the highest performance (accuracy = 0.731; AUC = 0.802). Overall, state-of-the-art automatic speech recognition (ASR) models outperformed other models.  \nIncorporating prosodic features such as pauses also improved classification in text-based approaches. Our work presents a comprehensive benchmarking framework built on state-of-the-art speech models and validated on a large, clinically relevant dataset, and demonstrates that foundation models can provide a scalable and cost-effective framework for early ADRD detection.  \nOur findings highlight the promise of speech-derived embeddingsas powerful non-invasive biomarkers for cognitive impairment.  \nFuture ADRD screening approaches should integrate both semantic and non-semantic features to improve generalizability and support broader clinical application.","cbCaivgXLdXegRN0","https://ap.wps.com/l/cbCaivgXLdXegRN0","pdf",2091640,"English","# Abstract\n## Biomarkers from Spontaneous Speech\n## Benchmarking Foundation Models\n## Performance Results and Feature Effects\n## Implications and Future Screening Directions","[{\"question\":\"Why is early detection important for Alzheimer’s disease and related dementias (ADRD)?\",\"answer\":\"Early detection supports timely intervention and care planning for these progressive neurodegenerative conditions.\"},{\"question\":\"How does the study use spontaneous speech for ADRD detection?\",\"answer\":\"It extracts acoustic biomarkers from spontaneous speech, including prosody, fluency, and pause patterns, to enable non-invasive diagnosis.\"},{\"question\":\"Which model and features improved performance in the benchmarking results?\",\"answer\":\"Whisper-medium achieved the highest speech-based performance, and incorporating prosodic features such as pauses improved classification for text-based approaches.\"}]","Benchmarking Foundation Models for Alzheimer’s Disease and Related Dementia Detection from Spontaneous Speech | PDF",1790642023]