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Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration

Abstract

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL’s current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here: https://demo.sci.tellapp.org/.

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References

  • Agurto, C., Pietrowicz, M., Norel, R., Eyigoz, E. K., Stanislawski, E., Cecchi, G., & Corcoran, C. (2020). Analyzing acoustic and prosodic fluctuations in free speech to predict psychosis onset in high-risk youths. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 5575–5579. https://doi.org/10.1109/embc44109.2020.9176841

    Article  PubMed  Google Scholar 

  • Ahmed, S., Haigh, A. M., de Jager, C. A., & Garrard, P. (2013). Connected speech as a marker of disease progression in autopsy-proven Alzheimer’s disease. Brain, 136(Pt 12), 3727–3737. https://doi.org/10.1093/brain/awt269

    Article  PubMed  PubMed Central  Google Scholar 

  • Al-Hameed, S., Benaissa, M., Christensen, H., Mirheidari, B., Blackburn, D., & Reuber, M. (2019). A new diagnostic approach for the identification of patients with neurodegenerative cognitive complaints. PLOS ONE, 14(5), e0217388. https://doi.org/10.1371/journal.pone.0217388

  • Association, Alzheimer’s. (2021). 2021 Alzheimer’s disease facts and figures. Alzheimers Dement, 17(3), 327–406. https://doi.org/10.1002/alz.12328

    Article  Google Scholar 

  • Arias-Vergara, T., Vasquez-Correa, J.C., Gollwitzer, S., Orozco-Arroyave, J.R., Schuster, M., Nöth, E. (2019). Multi-channel convolutional neural networks for automatic detection of speech deficits in cochlear implant users. In Nyström, I., Hernández Heredia, Y., Milián Núñez, V. (eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2019. Lecture Notes in Computer Science, 11896. Springer, Cham. https://doi.org/10.1007/978-3-030-33904-3_64

  • Ash, S., Evans, E., O'Shea, J., Powers, J., Boller, A., Weinberg, D., . . . Grossman, M. (2013). Differentiating primary progressive aphasias in a brief sample of connected speech. Neurology, 81(4), 329-336. https://doi.org/10.1212/WNL.0b013e31829c5d0e

  • Ash, S., Nevler, N., Phillips, J., Irwin, D. J., McMillan, C. T., Rascovsky, K., & Grossman, M. (2019). A longitudinal study of speech production in primary progressive aphasia and behavioral variant frontotemporal dementia. Brain Lang, 194, 46–57. https://doi.org/10.1016/j.bandl.2019.04.006

    Article  PubMed  PubMed Central  Google Scholar 

  • Ballard, K. J., Savage, S., Leyton, C. E., Vogel, A. P., Hornberger, M., & Hodges, J. R. (2014). Logopenic and Nonfluent Variants of Primary Progressive Aphasia Are Differentiated by Acoustic Measures of Speech Production. PLOS ONE, 9(2), e89864. https://doi.org/10.1371/journal.pone.0089864

  • Bedi, G., Carrillo, F., Cecchi, G. A., Slezak, D. F., Sigman, M., Mota, N. B., . . . Corcoran, C. M. (2015). Automated analysis of free speech predicts psychosis onset in high-risk youths. npj Schizophrenia, 1(1), 15030. https://doi.org/10.1038/npjschz.2015.30

  • Bedi, G., Cecchi, G. A., Slezak, D. F., Carrillo, F., Sigman, M., & de Wit, H. (2014). A window into the intoxicated mind? Speech as an index of psychoactive drug effects. Neuropsychopharmacology, 39(10), 2340–2348. https://doi.org/10.1038/npp.2014.80

    Article  PubMed  PubMed Central  Google Scholar 

  • Boschi, V., Catricalà, E., Consonni, M., Chesi, C., Moro, A., & Cappa, S. F. (2017). Connected speech in neurodegenerative language disorders: A Review. Frontiers in Psychology, 8(269). https://doi.org/10.3389/fpsyg.2017.00269

  • Bowen, L. K., Hands, G. L., Pradhan, S., & Stepp, C. E. (2013). Effects of Parkinson’s disease on fundamental frequency variability in running speech. J Med Speech Lang Pathol, 21(3), 235–244.

    PubMed  PubMed Central  Google Scholar 

  • Bredin, H., Yin, R., Coria, J. M., Gelly, G., Korshunov, P., Lavechin, M., . . . Gill, M. P. (2020). Pyannote.Audio: Neural Building Blocks for Speaker Diarization. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.

  • Busquet, F., Efthymiou, F., & Hildebrand, C. (2023). Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02139-9

    Article  PubMed  PubMed Central  Google Scholar 

  • Carrillo, F., Sigman, M., Fernández Slezak, D., Ashton, P., Fitzgerald, L., Stroud, J., . . . Carhart-Harris, R. L. (2018). Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. J Affect Disord, 230, 84-86. https://doi.org/10.1016/j.jad.2018.01.006

  • Chávez-Fumagalli, M. A., Shrivastava, P., Aguilar-Pineda, J. A., Nieto-Montesinos, R., Del-Carpio, G. D., Peralta-Mestas, A., . . . Lino Cardenas, C. L. (2021). Diagnosis of Alzheimer's disease in developed and developing countries: Systematic review and meta-analysis of diagnostic test accuracy. J Alzheimers Dis Rep, 5(1), 15-30. https://doi.org/10.3233/adr-200263

  • Cheng, S. T. (2017). Dementia Caregiver Burden: A Research Update and Critical Analysis. Curr Psychiatry Rep, 19(9), 64. https://doi.org/10.1007/s11920-017-0818-2

    Article  PubMed  PubMed Central  Google Scholar 

  • Cho, S., Nevler, N., Ash, S., Shellikeri, S., Irwin, D. J., Massimo, L., . . . Liberman, M. (2021a). Automated analysis of lexical features in frontotemporal degeneration. Cortex, 137, 215-231. https://doi.org/10.1016/j.cortex.2021.01.012

  • Cho, S., Shellikeri, S., Ash, S., Liberman, M. Y., Grossman, M., Nevler, N., & Nevler, N. (2021b). Automatic classification of AD versus FTLD pathology using speech analysis in a biologically confirmed cohort. Alzheimer's & Dementia, 17(S5), e052270. https://doi.org/10.1002/alz.052270

  • Cordella, C., Quimby, M., Touroutoglou, A., Brickhouse, M., Dickerson, B. C., & Green, J. R. (2019). Quantification of motor speech impairment and its anatomic basis in primary progressive aphasia. Neurology, 92(17), e1992–e2004. https://doi.org/10.1212/wnl.0000000000007367

    Article  PubMed  PubMed Central  Google Scholar 

  • Cox, R. V., Neto, S. F. D. C., Lamblin, C., & Sherif, M. H. (2009). ITU-T coders for wideband, superwideband, and fullband speech communication. IEEE Communications Magazine, 47(10), 106–109. https://doi.org/10.1109/MCOM.2009.5273816

    Article  Google Scholar 

  • Cummings, J., Lee, G., Ritter, A., Sabbagh, M., & Zhong, K. (2020). Alzheimer's disease drug development pipeline: 2020. Alzheimer's & Dementia: Translational Research & Clinical Interventions, 6(1), e12050. https://doi.org/10.1002/trc2.12050

  • de la Fuente García, S., Ritchie, C. W., & Luz, S. (2020). Artificial intelligence, speech, and language processing approaches to monitoring Alzheimer’s disease: A systematic review. Journal of Alzheimer’s Disease, 78, 1547–1574. https://doi.org/10.3233/JAD-200888

    Article  PubMed  PubMed Central  Google Scholar 

  • Dodge, H. H., Mattek, N., Gregor, M., Bowman, M., Seelye, A., Ybarra, O., . . . Kaye, J. A. (2015). Social markers of mild cognitive impairment: Proportion of word counts in free conversational speech. Curr Alzheimer Res, 12(6), 513-519. https://doi.org/10.2174/1567205012666150530201917

  • Dorsey, E. R., Elbaz, A., Nichols, E., Abd-Allah, F., Abdelalim, A., Adsuar, J. C., . . . Murray, C. J. L. (2018). Global, regional, and national burden of Parkinson's disease, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology, 17(11), 939-953. https://doi.org/10.1016/S1474-4422(18)30295-3

  • Dubois, B., Padovani, A., Scheltens, P., Rossi, A., & Dell’Agnello, G. (2016). Timely diagnosis for Alzheimer’s disease: A literature review on benefits and challenges. Journal of Alzheimer’s Disease, 49, 617–631. https://doi.org/10.3233/JAD-150692

    Article  PubMed  Google Scholar 

  • Ellis, C., Holt, Y. F., & West, T. (2015). Lexical diversity in Parkinson’s disease. Journal of Clinical Movement Disorders, 2(1), 5. https://doi.org/10.1186/s40734-015-0017-4

    Article  PubMed  PubMed Central  Google Scholar 

  • Eyben, F., Wöllmer, M., & Schuller, B. (2010). Opensmile: The munich versatile and fast open-source audio feature extractor. Proceedings of the 18th ACM international conference on Multimedia, Firenze, Italy. https://doi.org/10.1145/1873951.1874246

  • Eyigoz, E., Courson, M., Sedeño, L., Rogg, K., Orozco-Arroyave, J. R., Nöth, E., . . . García, A. M. (2020a). From discourse to pathology: Automatic identification of Parkinson's disease patients via morphological measures across three languages. Cortex, 132, 191-205. https://doi.org/10.1016/j.cortex.2020.08.020

  • Eyigoz, E., Mathur, S., Santamaria, M., Cecchi, G., & Naylor, M. (2020b). Linguistic markers predict onset of Alzheimer's disease. eClinicalMedicine. https://doi.org/10.1016/j.eclinm.2020.100583

  • Faroqi-Shah, Y., Sampson, M., Pranger, M., & Baughman, S. (2018). Cognitive control, word retrieval and bilingual aphasia: Is there a relationship? Journal of Neurolinguistics, 45, 95-109. https://doi.org/10.1016/j.jneuroling.2016.07.001

  • Faroqi-Shah, Y., Treanor, A., Ratner, N. B., Ficek, B., Webster, K., & Tsapkini, K. (2020). Using narratives in differential diagnosis of neurodegenerative syndromes. Journal of Communication Disorders, 85, 105994. https://doi.org/10.1016/j.jcomdis.2020.105994

  • Favaro, A., Moro-Velázquez, L., Butala, A., Motley, C., Cao, T., Stevens, R. D., . . . Dehak, N. (2023). Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease. Frontiers in Neurology, 14, 1142642. https://doi.org/10.3389/fneur.2023.1142642

  • Ferrante, F. J., Migeot, J. A., Birba, A., Amoruso, L., Pérez, G., Hesse, E., Tagliazucchi, E., Estienne, C., Serrano, C., Slachevsky, A., Matallana, D., Reyes, P., Ibáñez, A., Fittipaldi, S., González Campo, C. & García, A. M. (accepted). Multivariate word properties in fluency tasks reveal markers of Alzheimer’s dementia. Alzheimer’s & Dementia.

  • Fraser, K. C., Lundholm Fors, K., Eckerström, M., Öhman, F., & Kokkinakis, D. (2019). Predicting MCI status from multimodal language data using cascaded classifiers. Frontiers in Aging Neuroscience, 11, 205–205. https://doi.org/10.3389/fnagi.2019.00205

    Article  PubMed  PubMed Central  Google Scholar 

  • Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2016). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer’s Disease, 49(2), 407–422. https://doi.org/10.3233/jad-150520

    Article  PubMed  Google Scholar 

  • Fraser, K. C., Meltzer, J. A., Graham, N. L., Leonard, C., Hirst, G., Black, S. E., & Rochon, E. (2014). Automated classification of primary progressive aphasia subtypes from narrative speech transcripts. Cortex, 55, 43–60. https://doi.org/10.1016/j.cortex.2012.12.006

    Article  PubMed  Google Scholar 

  • García, A. M., Arias-Vergara, T., & J, C. V.-C., Nöth, E., Schuster, M., Welch, A. E., … Orozco-Arroyave, J. R. (2021). Cognitive determinants of dysarthria in Parkinson’s disease: An automated machine learning approach. Movement Disorders, 36, 2862–2873. https://doi.org/10.1002/mds.28751

    Article  PubMed  Google Scholar 

  • García, A. M., Carrillo, F., Orozco-Arroyave, J. R., Trujillo, N., Vargas Bonilla, J. F., Fittipaldi, S., . . . Cecchi, G. A. (2016). How language flows when movements don’t: An automated analysis of spontaneous discourse in Parkinson’s disease. Brain Lang, 162, 19-28. https://doi.org/10.1016/j.bandl.2016.07.008

  • García, A. M., Escobar-Grisales, D., Vásquez Correa, J. C., Bocanegra, Y., Moreno, L., Carmona, J., & Orozco-Arroyave, J. R. (2022a). Detecting Parkinson’s disease and its cognitive phenotypes via automated semantic analyses of action stories. npj Parkinson’s Disease, 8(1), 163. https://doi.org/10.1038/s41531-022-00422-8

  • García, A. M., Welch, A. E., Mandelli, M. L., Henry, M. L., Lukic, S., & Torres Prioris, M. J. (2022). Automated detection of speech timing alterations in autopsy-confirmed nonfluent/agrammatic variant primary progressive aphasia., 99(5), e500–e511. https://doi.org/10.1212/wnl.0000000000200750

    Article  Google Scholar 

  • García, A. M., de Leon, J., Tee, B. L., Blasi, D. E., & Gorno-Tempini, M. L. (2023). Speech and language markers of neurodegeneration: A call for global equity. Brain. https://doi.org/10.1093/brain/awad253

    Article  PubMed  PubMed Central  Google Scholar 

  • GBD 2017 US Neurological Disorders Collaborators. (2021). Burden of neurological disorders across the US from 1990–2017: A global burden of disease study. JAMA Neurology, 78(2), 165–176. https://doi.org/10.1001/jamaneurol.2020.4152

  • Gertken, L. M., Amengual, M., & Birdong, D. (2014). Assessing language dominance with the Bilingual Language Profile. In P. Leclercq, A. Edmonds, & H. Hilton (Eds.), Measuring L2 Proficiency: Perspectives from SLA. Multilingual Matters.

  • X. Hao, X. Su, R. Horaud and X. Li (201). Fullsubnet: A full-band and sub-band fusion model for real-time single-channel speech enhancement. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 2021, pp. 6633-6637.  https://doi.org/10.1109/ICASSP39728.2021.9414177

  • Haulcy, R., & Glass, J. (2020). Classifying Alzheimer's disease using audio and text-based representations of speech. Frontiers in Psychology, 11, 624137. https://doi.org/10.3389/fpsyg.2020.624137

  • Hecker, P., Steckhan, N., Eyben, F., Schuller, B. W., & Arnrich, B. (2022). Voice analysis for neurological disorder recognition: A systematic review and perspective on emerging trends. Frontiers in Digital Health, 4, 842301. https://doi.org/10.3389/fdgth.2022.842301

  • Isaacson, R. S., Ganzer, C. A., Hristov, H., Hackett, K., Caesar, E., Cohen, R., . . . Krikorian, R. (2018). The clinical practice of risk reduction for Alzheimer's disease: A precision medicine approach. Alzheimer’s & Dementia, 14(12), 1663-1673. https://doi.org/10.1016/j.jalz.2018.08.004

  • Isaacson, R. S., Hristov, H., Saif, N., Hackett, K., Hendrix, S., Melendez, J., . . . Krikorian, R. (2019). Individualized clinical management of patients at risk for Alzheimer's dementia. Alzheimer’s & Dementia, 15(12), 1588-1602. https://doi.org/10.1016/j.jalz.2019.08.198

  • Jonell, P., Moëll, B., Håkansson, K., Henter, G. E., Kucherenko, T., Mikheeva, O., . . . Beskow, J. (2021). Multimodal capture of patient behaviour for improved detection of early dementia: Clinical feasibility and preliminary results. Frontiers in Computer Science, 3. https://doi.org/10.3389/fcomp.2021.642633

  • Laske, C., Sohrabi, H. R., Frost, S. M., López-de-Ipiña, K., Garrard, P., Buscema, M., . . . O'Bryant, S. E. (2015). Innovative diagnostic tools for early detection of Alzheimer's disease. Alzheimer’s & Dementia, 11(5), 561-578. https://doi.org/10.1016/j.jalz.2014.06.004

  • Li, J., Song, K., Li, J., Zheng, B., Li, D. S., Wu, X., . . . Meng, H. M. (2023). Leveraging pretrained representations with task-related keywords for Alzheimer's disease detection. ArXiv, abs/2303.08019.

  • Luz, S., Haider, F., de la Fuente Garcia, S., Fromm, D., & MacWhinney, B. (2021). Editorial: Alzheimer's dementia recognition through spontaneous speech. Frontiers in Computer Science, 3. https://doi.org/10.3389/fcomp.2021.780169

  • Luz, S., Haider, F., de la Fuente, S., Fromm, D., & MacWhinney, B. (2020). Alzheimer’s dementia recognition through spontaneous speech: The ADReSS challenge. Proceedings of Interspeech, 2020, 2172–2176. https://doi.org/10.21437/Interspeech.2020-2571

    Article  Google Scholar 

  • MacFarlane, H., Salem, A. C., Chen, L., Asgari, M., & Fombonne, E. (2022). Combining voice and language features improves automated autism detection. Autism Research, 15(7), 1288–1300. https://doi.org/10.1002/aur.2733

    Article  PubMed  PubMed Central  Google Scholar 

  • Macoir, J., Hudon, C., Tremblay, M. P., Laforce, R. J., & Wilson, M. A. (2019). The contribution of semantic memory to the recognition of basic emotions and emotional valence: Evidence from the semantic variant of primary progressive aphasia. Social Neuroscience, 14(6), 705–716. https://doi.org/10.1080/17470919.2019.1577295

    Article  PubMed  Google Scholar 

  • Meilán, J. J., Martínez-Sánchez, F., Carro, J., López, D. E., Millian-Morell, L., & Arana, J. M. (2014). Speech in Alzheimer’s disease: Can temporal and acoustic parameters discriminate dementia? Dementia and Geriatric Cognitive Disorders, 37(5–6), 327–334. https://doi.org/10.1159/000356726

    Article  PubMed  Google Scholar 

  • Mendez, M. F., Carr, A. R., & Paholpak, P. (2017). Psychotic-like speech in frontotemporal dementia. Journal of Neuropsychiatry and Clinical Neuroscience, 29(2), 183–185. https://doi.org/10.1176/appi.neuropsych.16030058

    Article  Google Scholar 

  • Mota, N. B., Copelli, M., & Ribeiro, S. (2017). Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance. npj Schizophrenia, 3(1), 18. https://doi.org/10.1038/s41537-017-0019-3

  • Mota, N. B., Furtado, R., Maia, P. P. C., Copelli, M., & Ribeiro, S. (2014). Graph analysis of dream reports is especially informative about psychosis [Article]. Scientific Reports, 4, 3691. https://doi.org/10.1038/srep03691

    Article  PubMed  PubMed Central  Google Scholar 

  • Mota, N. B., Vasconcelos, N. A., Lemos, N., Pieretti, A. C., Kinouchi, O., Cecchi, G. A., . . . Ribeiro, S. (2012). Speech graphs provide a quantitative measure of thought disorder in psychosis. PLoS One, 7(4), e34928. https://doi.org/10.1371/journal.pone.0034928

  • Nandi, A., Counts, N., Chen, S., Seligman, B., Tortorice, D., Vigo, D., & Bloom, D. E. (2022). Global and regional projections of the economic burden of Alzheimer's disease and related dementias from 2019 to 2050: A value of statistical life approach. eClinicalMedicine, 51. https://doi.org/10.1016/j.eclinm.2022.101580

  • Nevler, N., Ash, S., Irwin, D. J., Liberman, M., & Grossman, M. (2019). Validated automatic speech biomarkers in primary progressive aphasia. Annals of Clinical and Translational Neurology, 6(1), 4–14. https://doi.org/10.1002/acn3.653

    Article  PubMed  Google Scholar 

  • Nevler, N., Ash, S., Jester, C., Irwin, D. J., Liberman, M., & Grossman, M. (2017). Automatic measurement of prosody in behavioral variant FTD. Neurology, 89(7), 650–656. https://doi.org/10.1212/wnl.0000000000004236

    Article  PubMed  PubMed Central  Google Scholar 

  • Nichols, E., Steinmetz, J. D., Vollset, S. E., Fukutaki, K., Chalek, J., Abd-Allah, F., . . . Vos, T. (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. The Lancet Public Health, 7(2), e105-e125. https://doi.org/10.1016/S2468-2667(21)00249-8

  • Norel, R., Agurto, C., Heisig, S., Rice, J. J., Zhang, H., Ostrand, R., . . . Cecchi, G. A. (2020). Speech-based characterization of dopamine replacement therapy in people with Parkinson’s disease. npj Parkinson’s Disease, 6(12). https://doi.org/10.1038/s41531-020-0113-5

  • Orimaye, S. O., Wong, J. S. M., Golden, K. J., Wong, C. P., & Soyiri, I. N. (2017). Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers. BMC Bioinformatics, 18(1), 34–34. https://doi.org/10.1186/s12859-016-1456-0

    Article  PubMed  PubMed Central  Google Scholar 

  • Orimaye, S. O., Wong, J. S.-M., & Wong, C. P. (2018). Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia. PLOS ONE, 13(11), e0205636. https://doi.org/10.1371/journal.pone.0205636

  • Padró, L. & Stanilovsky, E. (2012). FreeLing 3.0: Towards wider multilinguality. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), 2473-2479, Istanbul, Turkey. European Language Resources Association (ELRA).

  • Paek, E. J. (2021). Emotional valence affects word retrieval during verb fluency tasks in Alzheimer's dementia. Frontiers in Psychology, 12, 777116. https://doi.org/10.3389/fpsyg.2021.777116

  • Parra, M. A., Baez, S., Allegri, R., Nitrini, R., Lopera, F., Slachevsky, A., . . . Ibáñez, A. (2018). Dementia in Latin America: Assessing the present and envisioning the future. Neurology, 90(5), 222-231. https://doi.org/10.1212/wnl.0000000000004897

  • Parra, M., Orellana, P., León, T., Cabello, V., Henriquez, F., Gomez, R., …, Durán-Aniotz, C. (2023). Biomarkers for dementia in Latin American countries: Gaps and opportunities. Alzheimer's & Dementia, 19(2), 721-735. https://doi.org/10.1002/alz.12757

  • Pell, M. D., Cheang, H. S., & Leonard, C. L. (2006). The impact of Parkinson’s disease on vocal-prosodic communication from the perspective of listeners. Brain & Language, 97(2), 123–134. https://doi.org/10.1016/j.bandl.2005.08.010

    Article  Google Scholar 

  • Pérez-Toro, P. A., Klumpp, P., Hernández, A., Arias-Vergara, T., Lillo, P., Slachevsky, A., . . . Orozco-Arroyave, J. R. (2022). Alzheimer’s detection from English to Spanish using acoustic and linguistic embeddings. 23rd Interspeech Conference, Incheon, Korea, 2483-2487.

  • Radford, A., Kim, J. W., Xu, T., Brockman, G., McLeavey, C., & Sutskever, I. (2023). Robust speech recognition via large-scale weak supervision. Proceedings of the 40th International Conference on Machine Learning, 202, 28492-28518. Accessed on June 6, 2023.

  • Rentoumi, V., Raoufian, L., Ahmed, S., de Jager, C. A., & Garrard, P. (2014). Features and machine learning classification of connected speech samples from patients with autopsy proven Alzheimer’s disease with and without additional vascular pathology. Journal of Alzheimer’s Disease, 42(Suppl 3), S3-17. https://doi.org/10.3233/jad-140555

    Article  PubMed  Google Scholar 

  • Riley, K. P., Snowdon, D. A., Desrosiers, M. F., & Markesbery, W. R. (2005). Early life linguistic ability, late life cognitive function, and neuropathology: Findings from the Nun Study. Neurobiology of Aging, 26(3), 341-347. https://doi.org/10.1016/j.neurobiolaging.2004.06.019

  • Rusz, J., & Tykalová, T. (2021). Does cognitive impairment influence motor speech performance in de novo Parkinson's disease? Movement Disorders, 36(12), 2980-2982. https://doi.org/10.1002/mds.28836

  • Rusz, J., Cmejla, R., Tykalova, T., Ruzickova, H., Klempir, J., Majerova, V., . . . Ruzicka, E. (2013). Imprecise vowel articulation as a potential early marker of Parkinson's disease: Effect of speaking task. J Acoust Soc Am, 134(3), 2171-2181. https://doi.org/10.1121/1.4816541

  • Sanz, C., Carrillo, F., Slachevsky, A., Forno, G., Gorno Tempini, M. L., Villagra, R., . . . García, A. M. (2022). Automated text-level semantic markers of Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 14(1), e12276. https://doi.org/10.1002/dad2.12276

  • Seixas Lima, B., Levine, B., Graham, N. L., Leonard, C., Tang-Wai, D., Black, S., & Rochon, E. (2020). Impaired coherence for semantic but not episodic autobiographical memory in semantic variant primary progressive aphasia. Cortex, 123, 72–85. https://doi.org/10.1016/j.cortex.2019.10.008

    Article  PubMed  Google Scholar 

  • Singh, S., Bucks, R. S., & Cuerden, J. M. (2001). Evaluation of an objective technique for analysing temporal variables in DAT spontaneous speech. Aphasiology, 15(6), 571–583. https://doi.org/10.1080/02687040143000041

    Article  Google Scholar 

  • Themistocleous, C., Webster, K., Afthinos, A., & Tsapkini, K. (2021). Part of speech production in patients with primary progressive aphasia: An analysis based on natural language processing. Am J Speech Lang Pathol, 30(1s), 466–480. https://doi.org/10.1044/2020_ajslp-19-00114

    Article  PubMed  Google Scholar 

  • Tosto, G., Gasparini, M., Lenzi, G. L., & Bruno, G. (2011). Prosodic impairment in Alzheimer’s disease: Assessment and clinical relevance. J Neuropsychiatry Clin Neurosci, 23(2), E21-23. https://doi.org/10.1176/jnp.23.2.jnpe21

    Article  PubMed  Google Scholar 

  • Van Der Donckt, J., Kappen, M., Degraeve, V., Demuynck, K., Vanderhasselt, M., & Van Hoecke, S. (2023). Ecologically valid speech collection in behavioral research: The Ghent Semi-spontaneous Speech Paradigm (GSSP). Behavior Research Methods, forthcoming. https://doi.org/10.31234/osf.io/e2qxw

  • Wang, J., Zhang, L., Liu, T., Pan, W., Hu, B., & Zhu, T. (2019). Acoustic differences between healthy and depressed people: A cross-situation study. BMC Psychiatry, 19(1), 300. https://doi.org/10.1186/s12888-019-2300-7

    Article  PubMed  PubMed Central  Google Scholar 

  • Webber, J., Parastatidis, S., & Robinson, I. (2010). REST in Practice: Hypermedia and Systems Architecture. O'Reilly Media, Incorporated. https://books.google.cl/books?id=5CjJcil4UfMC

  • Wilson, S. M., Henry, M. L., Besbris, M., Ogar, J. M., Dronkers, N. F., Jarrold, W., . . . Gorno-Tempini, M. L. (2010). Connected speech production in three variants of primary progressive aphasia. Brain, 133(Pt 7), 2069-2088. https://doi.org/10.1093/brain/awq129

  • Zimmerer, V. C., Hardy, C. J. D., Eastman, J., Dutta, S., Varnet, L., Bond, R. L., . . . Varley, R. A. (2020). Automated profiling of spontaneous speech in primary progressive aphasia and behavioral-variant frontotemporal dementia: An approach based on usage-frequency. Cortex. https://doi.org/10.1016/j.cortex.2020.08.027

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Funding

Adolfo García is an Atlantic Fellow at the Global Brain Health Institute (GBHI) and is partially supported by the National Institute On Aging of the National Institutes of Health (R01AG075775); ANID (FONDECYT Regular 1210176, 1210195); GBHI, Alzheimer’s Association, and Alzheimer’s Society (Alzheimer’s Association GBHI ALZ UK-22-865742); Universidad de Santiago de Chile (DICYT 032351GA_DAS); and the Multi-partner Consortium to Expand Dementia Research in Latin America (ReDLat), which is supported by the Fogarty International Center and the National Institutes of Health, the National Institute on Aging (R01AG057234, R01AG075775, R01AG21051, and CARDS-NIH), Alzheimer’s Association (SG-20-725707), Rainwater Charitable Foundation’s Tau Consortium, the Bluefield Project to Cure Frontotemporal Dementia, and the Global Brain Health Institute. The contents of this publication are solely the responsibility of the authors and do not represent the official views of these institutions.

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Contributions

Adolfo M. García: conception, organization, figure design, writing of the first draft. Fernando Johann: review and critique. Raúl Echegoyen: review and critique. Cecilia Calcaterra: review and critique. Pablo Riera: writing of the first draft, review and critique. Laouen Belloli: writing of the first draft, review and critique. Facundo Carrillo: writing of the first draft, review and critique.

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Correspondence to Adolfo M. García.

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No approval of research ethics committees was required to accomplish the goals of this study, as it only describes software development and refers to previous literature.

Competing interests

Adolfo M. García, Fernando Johann, and Cecilia Calcaterra have received financial support from TELL SA. Raúl Echegoyen is consultant to TELL SA. Laouen Belloli, Pablo Riera, and Facundo Carrillo declare that they have no financial interest.

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García, A.M., Johann, F., Echegoyen, R. et al. Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration. Behav Res (2023). https://doi.org/10.3758/s13428-023-02240-z

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