Spoken Language Derived Measures for Detecting Mild Cognitive Impairment
Open accessAbstract
Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Given the audio and transcript of a spoken narrative recall task, a range of markers are automatically derived. These markers include speech features such as pause frequency and duration, and many linguistic complexity measures. We examine measures calculated from manually annotated time alignments (of the transcript with the audio) and syntactic parse trees, as well as the same measures calculated from automatic (forced) time alignments and automatic parses. We show statistically significant differences between clinical subject groups for a number of measures. These differences are largely preserved with automation. We then present classification results, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores. Our results indicate that using multiple, complementary measures can aid in automatic detection of MCI.
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Acoustic, syntactic, and semantic features, including grammatical complexity and information content, can correlate strongly with cognitive decline.
“Acoustic, syntactic, and semantic features, including grammatical complexity and information content, can correlate strongly with cognitive decline [11,32].”
From A Conversational Robot for Older Adults with Alzheimer's Disease (2020) · cites this paper as [32]
The Experimental Results (section IV.D; page not provided) report significant differences between healthy elderly subjects and those with mild cognitive impairment for some speech, syntactic, and content-density measures. But idea density did not differ significantly, and the study compares groups rather than measuring correlations with cognitive decline over time. The claim overgeneralizes both the measures and the outcome. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOther language tasks, such as naming, verbal fluency, and story recall, have also been used for automatic monitoring of AD.
“Other language tasks, such as the naming task [20], the verbal fluency task [18], and the story recall task [32], have also been used for automatic monitoring of AD.”
From A Conversational Robot for Older Adults with Alzheimer's Disease (2020) · cites this paper as [32]
The Abstract and §II.B describe automatically derived measures from a story-recall task, supporting the portion of the sentence attached to this citation. But the study tests detection of mild cognitive impairment, not automatic monitoring of diagnosed Alzheimer’s disease; naming and verbal-fluency tasks have separate citations in the citing sentence. The indexed passages provide section labels but no page numbers. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe linguistic analysis for cognitive monitoring followed Fraser et al. [11] and Roark et al. [32] and was restricted to features automatically extractable from transcriptions.
“Our linguistic analysis for cognitive monitoring has followed the work of Fraser et al. [11] and Roark et al. [32] and is restricted to examining features that can be automatically extracted from transcriptions of conversations.”
From A Conversational Robot for Older Adults with Alzheimer's Disease (2020) · cites this paper as Roark et al. [32]
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Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856 · see citing works
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,010 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,803 · see citing works
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,003 · see citing works
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882 · see citing works
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980 · see citing works
Building a Large Annotated Corpus of English: The Penn Treebank
Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405 · see citing works
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809 · see citing works
Making Large-Scale SVM Learning PracticalPreprint
Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,514 · see citing works
Linguistic complexity: locality of syntactic dependencies
Gibson, E. (1998) · Cognition · doi:10.1016/s0010-0277(98)00034-1 · Cited by 2,537 · see citing works
SWITCHBOARD: telephone speech corpus for research and development
Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992) · [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1992.225858 · Cited by 2,134 · cited 1× · see citing works
“We followed the utterance segmentation rules outlined in the 2004 EARS Official Annotation Guidelines [22] for conversational speech, such as that found in the Switchboard corpus [ 23 ].”
MethodSignal detection theory and ROC analysis
Egan, J. P. (1975) · Academic Press eBooks · Cited by 1,656 · see citing works
(1999) · Cited by 1,521 · see citing works
Welsh, K. A., Butters, N., Mohs, R. C., Beekly, D., Edland, S., Fillenbaum, G., & Heyman, A. (1994) · Neurology · doi:10.1212/wnl.44.4.609 · Cited by 951 · see citing works
Snowdon, D. A. (1996) · JAMA · doi:10.1001/jama.1996.03530310034029 · Cited by 696 · see citing works
Incidence and outcome of mild cognitive impairment in a population-based prospective cohort
Larrieu, S., Letenneur, L., Orgogozo, J., Fabrigoule, C., Amieva, H., Carret, N. L., Barberger‐Gateau, P., & Dartigues, J. (2002) · Neurology · doi:10.1212/01.wnl.0000034176.07159.f8 · Cited by 663 · see citing works
Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment
DeCarli, C. S. (2002) · The Lancet Neurology · doi:10.1016/s1474-4422(03)00262-x · Cited by 569 · see citing works
Statistical decision-tree models for parsing
Magerman, D. M. (1995) · Proceedings of the 33rd annual meeting on Association for Computational Linguistics - · doi:10.3115/981658.981695 · Cited by 567 · see citing works
A model and an hypothesis for language structure
Yngve, V. H. (1960) · Cited by 522 · see citing works
The Relation between Age and Mean Length of Utterance in Morphemes
Miller, J. F., & Chapman, R. S. (1981) · Journal of Speech Language and Hearing Research · doi:10.1044/jshr.2402.154 · Cited by 518 · see citing works
Scarborough, H. S. (1990) · Applied Psycholinguistics · doi:10.1017/s0142716400008262 · Cited by 517 · see citing works
Building a Large Annotated Corpus of English: The
MarcusMitchell, P., Ann, M., & SantoriniBeatrice (1993) · Computational Linguistics · doi:10.5555/972470.972475 · Cited by 495 · see citing works
Procedure for quantitatively comparing the syntactic coverage of English grammars
Klavans, J. L., Liberman, M., Marcus, M. P., Roukos, S., Santorini, B., Strzalkowski, T., Abney, S. P., Flickenger, S., Gdaniec, C., Grishman, C., et al. (1991) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/112405.112467 · Cited by 432 · see citing works
Mild cognitive impairment: conceptual basis and current nosological status
Ritchie, K. A., & Touchon, J. (2000) · The Lancet · doi:10.1016/s0140-6736(99)06155-3 · Cited by 386 · see citing works
Clinical Dementia Rating training and reliability in multicenter studies
Morris, J. C., Ernesto, C., Schafer, K. A., Coats, M. A., Leon, S. A., Sano, M. C., Thal, L. J., & Woodbury, P. (1997) · Neurology · doi:10.1212/wnl.48.6.1508 · Cited by 376 · see citing works
Automatic measurement of propositional idea density from part-of-speech tagging
Brown‐Johnson, C. G., Snodgrass, T., Kemper, S. J., Herman, R. E., & Covington, M. A. (2008) · Behavior Research Methods · doi:10.3758/brm.40.2.540 · Cited by 228 · see citing works
Mild cognitive impairment : aging to Alzheimer's disease
Petersen, R. C. (2003) · Cited by 226 · see citing works
Frazier, L. (1985) · Cambridge University Press eBooks · doi:10.1017/cbo9780511597855.005 · Cited by 199 · see citing works
Edit detection and parsing for transcribed speech
Charniak, E., & Johnson, M. S. (2001) · doi:10.3115/1073336.1073352 · Cited by 160 · see citing works
Dementia Assessment in Primary Care: Results From a Study in Three Managed Care Systems
Boise, L., Neal, M. B., & Kaye, J. A. (2004) · The Journals of Gerontology Series A · doi:10.1093/gerona/59.6.m621 · Cited by 133 · see citing works
Self-citationOn the Preservation of Syntax in Alzheimer's Disease
Kemper, S. J. (1993) · Archives of Neurology · doi:10.1001/archneur.1993.00540010075021 · Cited by 129 · see citing works
Deterministic left corner parsing
Rosenkrantz, D. J., & Lewis, P. (1970) · Annual Symposium on Switching & Automata Theory · doi:10.1109/swat.1970.5 · Cited by 128 · see citing works
Rosenberg, S. D., & Abbeduto, L. (1987) · Applied Psycholinguistics · doi:10.1017/s0142716400000047 · Cited by 102 · see citing works
Syntactic complexity measures for detecting mild cognitive impairment
Roark, B., Mitchell, M., & Hollingshead, K. (2007) · doi:10.3115/1572392.1572394 · Cited by 92 · see citing works
Self-citationNatural history of cognitive decline in the old old
Howieson, D. B., Camicioli, R. M., Quinn, J. F., Silbert, L. C., Care, B., Moore, M. M., Dame, A., Sexton, G. J., & Kaye, J. A. (2003) · Neurology · doi:10.1212/01.wnl.0000063317.44167.5c · Cited by 91 · see citing works
Self-citationAutomatic measurement of syntactic development in child language
Sagae, K., Lavie, A., & MacWhinney, B. (2005) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1219840.1219865 · Cited by 81 · see citing works
MAP adaptation of stochastic grammars
Bacchiani, M., Riley, M., Roark, B., et al. (2005) · Computer Speech & Language · doi:10.1016/j.csl.2004.12.001 · Cited by 72 · see citing works
Self-citationEvaluation of an objective technique for analysing temporal variables in DAT spontaneous speech
Singh, S., Bucks, R. S., & Cuerden, J. M. (2001) · Aphasiology · doi:10.1080/02687040143000041 · Cited by 67 · see citing works
Speaker-independent phoneme alignment using transition-dependent states
Hosom, J.-P. (2008) · Speech Communication · doi:10.1016/j.specom.2008.11.003 · Cited by 59 · see citing works
Self-citationOral language and alzheimer's disease: A reduction in syntactic complexity
Lyons, K. E., Kemper, S. J., LaBarge, E., Ferraro, F. R., Balota, D. A., & Storandt, M. (1994) · Aging Neuropsychology and Cognition · doi:10.1080/13825589408256581 · Cited by 59 · see citing works
On the structural complexity of natural language sentences
Lin, D. (1996) · Proceedings of the 16th conference on Computational linguistics - · doi:10.3115/993268.993295 · Cited by 39 · see citing works
Gabani, K., Sherman, M., Solorio, T., Liu, Y., Bedore, L. M., & Peña, E. D. (2009) · North American Chapter of the Association for Computational Linguistics · doi:10.3115/1620754.1620762 · Cited by 35 · see citing works
An Alternative Ranking Problem for Search Engines
Cortes, C., Mohri, M., & Rastogi, A. K. (2007) · Springer eBooks · doi:10.1007/978-3-540-72845-0_1 · Cited by 29 · see citing works
Un modèle et une hypothèse pour la structure du langage
Yngve, V. H. (1974) · Mouton eBooks · doi:10.1515/9783110814156.365 · Cited by 7 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [1]Roark B Hosom J Mitchell M Kaye J Automatically derived spoken language markers for detecting mild cognitive impairment Proc. 2nd Int. Conf. Technol. Aging (ICTA) 2007
- [20]Wechsler D Wechsler Memory Scale—Third Edition Manual San Antonio, TX The Psychological Corp 1997
- [44]Pahikkala T Airola A Boberg J Salakoski T Exact and efficient leave-pair-out cross-validation for ranking RLS Proc. AKRR08 2008 1 8
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Linguistic Features Identify Alzheimer’s Disease in Narrative Speech
Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-150520 · Cited by 797 · see citing works
Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease
König, A., Satt, A., Sorin, A., Hoory, R., Toledo‐Ronen, O., Derreumaux, A., Manera, V., Verhey, F., Aalten, P., Robert, P. H., et al. (2015) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2014.11.012 · Cited by 380 · see citing works
Toth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatloczki, G., Banreti, Z., Pakaski, M., & Kalman, J. (2018) · Current Alzheimer Research · doi:10.2174/1567205014666171121114930 · Cited by 290 · see citing works
Automated classification of primary progressive aphasia subtypes from narrative speech transcripts
Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014) · Cortex · doi:10.1016/j.cortex.2012.12.006 · Cited by 229 · see citing works
Gosztolya, G., Vincze, V., Tóth, L., Pákáski, M., Kálmán, J., & Hoffmann, I. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2018.07.007 · Cited by 196 · see citing works
Beltrami, D., Gagliardi, G., Rossini Favretti, R., Ghidoni, E., Tamburini, F., & Calzà, L. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00369 · Cited by 175 · see citing works
Quantifying the Language of Schizophrenia in Social Media
Mitchell, M., Hollingshead, K., & Coppersmith, G. (2015) · Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w15-1202 · Cited by 172 · see citing works
Self-citationPredicting probable Alzheimer's disease using linguistic deficits and biomarkers
Orimaye, S. O., Wong, J. S.-M., Golden, K. J., Wong, C. P., & Soyiri, I. N. (2017) · BMC Bioinformatics · doi:10.1186/s12859-016-1456-0 · Cited by 158 · see citing works
Detecting Dementia Through Interactive Computer Avatars
Tanaka, H., Adachi, H., Ukita, N., Ikeda, M., Kazui, H., Kudo, T., & Nakamura, S. (2017) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2017.2752152 · Cited by 127 · see citing works
Automatic quantitative analysis of spontaneous aphasic speech
Le, D., Licata, K., & Mower Provost, E. (2018) · Speech Communication · doi:10.1016/j.specom.2018.04.001 · Cited by 97 · see citing works
A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders
Voleti, R., Liss, J. M., & Berisha, V. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2952087 · Cited by 96 · see citing works
Rudzicz, F., Wang, R., Begum, M., & Mihailidis, A. (2015) · ACM Transactions on Accessible Computing · doi:10.1145/2744206 · Cited by 83 · see citing works
Dementia detection using automatic analysis of conversations
Mirheidari, B., Blackburn, D., Walker, T., Reuber, M., & Christensen, H. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2018.07.006 · Cited by 82 · see citing works
Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task
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Conversational Affective Social Robots for Ageing and Dementia Support
Lima, M. R., Wairagkar, M., Gupta, M., Rodriguez y Baena, F., Barnaghi, P., Sharp, D. J., & Vaidyanathan, R. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3115228 · Cited by 75 · see citing works
Yancheva, M., Fraser, K., & Rudzicz, F. (2015) · Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies · doi:10.18653/v1/w15-5123 · Cited by 76 · see citing works
Severity of Alzheimer’s disease and language features in picture descriptions
Kavé, G., & Dassa, A. (2018) · Aphasiology · doi:10.1080/02687038.2017.1303441 · Cited by 72 · see citing works
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Pou-Prom, C., Raimondo, S., & Rudzicz, F. (2020) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3380785 · Cited by 59 · see citing works
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Zhou, L., Fraser, K. C., & Rudzicz, F. (2016) · Interspeech 2016 · doi:10.21437/interspeech.2016-1228 · Cited by 62 · see citing works
Martinc, M., Haider, F., Pollak, S., & Luz, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.642647 · Cited by 55 · see citing works
“A study by Roark et al. (2011) used natural language processing (NLP) and automatic speech recognition (ASR) to automatically annotate and time-align a few spoken language features (pause frequency and duration), and compared these methods to manual analysis.”
MethodOrimaye, S. O., Wong, J. S.-M., & Wong, C. P. (2018) · PLOS ONE · doi:10.1371/journal.pone.0205636 · Cited by 52 · see citing works
Dialogue Systems and Conversational Agents for Patients with Dementia: The Human–Robot Interaction
Russo, A., D'Onofrio, G., Gangemi, A., Giuliani, F., Mongiovi, M., Ricciardi, F., Greco, F., Cavallo, F., Dario, P., Sancarlo, D., et al. (2019) · Rejuvenation Research · doi:10.1089/rej.2018.2075 · Cited by 47 · see citing works
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Fristed, E., Skirrow, C., Meszaros, M., Lenain, R., Meepegama, U., Cappa, S., Aarsland, D., & Weston, J. (2022) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12366 · Cited by 47 · see citing works
Al-Hameed, S., Benaissa, M., Christensen, H., Mirheidari, B., Blackburn, D., & Reuber, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0217388 · Cited by 37 · see citing works
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Sadeghian, R., Schaffer, J. D., & Zahorian, S. A. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-1712 · Cited by 40 · see citing works
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Yu, B., Quatieri, T. F., Williamson, J. R., & Mundt, J. C. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-741 · Cited by 28 · see citing works
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Komeili, M., Pou-Prom, C., Liaqat, D., Fraser, K. C., Yancheva, M., & Rudzicz, F. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212342 · Cited by 23 · see citing works
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Chandler, C., Diaz‐Asper, C., Turner, R. S., Reynolds, B., & Elvevåg, B. (2023) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1002/dad2.12516 · Cited by 24 · see citing works
Shah, Z., Qi, S.-A., Wang, F., Farrokh, M., Tasnim, M., Stroulia, E., Greiner, R., Plitsis, M., & Katsamanis, A. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10095593 · Cited by 19 · see citing works
Agmon, G., Pradhan, S., Ash, S., Nevler, N., Liberman, M., Grossman, M., & Cho, S. (2024) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2023_jslhr-23-00009 · Cited by 17 · see citing works
de la Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020) · Journal of Alzheimer's Disease · doi:10.3233/jad-200888 · Cited by 5 · see citing works
- Authors
- Roark, B., Mitchell, M., Hosom, J.-P., Hollingshead, K., & Kaye, J.
- Affiliations
- University of Aberdeen (GB)
- Oregon Health & Science University (US)
- Venue
- IEEE Transactions on Audio, Speech, and Language Processing
- Published
- 2011-09
- Volume
- 19
- Issue
- 7
- Pages
- 2081-2090
- ISSN
- 1558-7916 (ISSN-L)
- 1558-7924
- PMC
- PMC3244269
- OpenAlex
- W2136914353
- Semantic Scholar
- 343a5ab97e47368da0aa7b50256736297cbb9ce6
- Cited by
- 355
- References
- 46
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 22 · 2023: 38 · 2022: 25 · 2021: 35 · 2020: 29 · 2019: 29 · 2018: 26 · 2017: 26 · 2016: 25 · 2015: 23 · 2014: 19 · 2013: 10 · 2012: 6
- FWCI
- 7.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Audio Speech and Language Processing (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Santen
- National Institute on Aging (R01AG024059, P30AG008017, P30AG024978)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- mild cognitive impairment, spoken language features, automated speech analysis, narrative recall, pause frequency, linguistic complexity, temporal alignment, syntactic analysis, neuropsychological test scores, older adults, speech biomarkers
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