Acknowledgement
This research was supported by grants from the Ministry of SMEs and Startups (Project Number: S3079103) and the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (Project Number: NRF2017S1A6A3A01078538).
References
- Selkoe DJ, Lansbury PJ. Alzheimer's disease is the most common neurodegenerative disorder. In: Siegel GJ, Agranoff BW, Albers RW, Fisher SK, Uhler MD, editors. Basic Neurochemistry: Molecular, Cellular and Medical Aspects. 6th ed. Philadelphia: Lippincott-Raven, 1999.
- World Health Organization. Dementia. Key Facts. Vol 2023. Geneva: World Health Organization, 2023.
- Knopman DS, DeKosky ST, Cummings JL, Chui H, Corey-Bloom J, Relkin N, et al. Practice parameter: diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2001;56:1143-1153.
- Waldemar G, Dubois B, Emre M, Georges J, McKeith IG, Rossor M, et al. Recommendations for the diagnosis and management of Alzheimer's disease and other disorders associated with dementia: EFNS guideline. Eur J Neurol 2007;14:e1-e26.
- Konig A, Satt A, Sorin A, Hoory R, Toledo-Ronen O, Derreumaux A, et al. Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease. Alzheimers Dement (Amst) 2015;1:112-124.
- Themistocleous C, Eckerstrom M, Kokkinakis D. Voice quality and speech fluency distinguish individuals with mild cognitive impairment from healthy controls. PLoS One 2020;15:e0236009.
- Garrard P, Maloney LM, Hodges JR, Patterson K. The effects of very early Alzheimer's disease on the characteristics of writing by a renowned author. Brain 2005;128:250-260.
- Mahon E, Lachman ME. Voice biomarkers as indicators of cognitive changes in middle and later adulthood. Neurobiol Aging 2022;119:22-35.
- Houde JF, Jordan MI. Sensorimotor adaptation of speech I: compensation and adaptation. J Speech Lang Hear Res 2002;45:295-310.
- Purcell DW, Munhall KG. Compensation following real-time manipulation of formants in isolated vowels. J Acoust Soc Am 2006;119:2288-2297.
- Houde JF, Nagarajan SS, Sekihara K, Merzenich MM. Modulation of the auditory cortex during speech: an MEG study. J Cogn Neurosci 2002;14:1125-1138.
- Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 1991;82:239-259.
- Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria. Lancet Neurol 2007;6:734-746.
- First MB, Pincus HA. The DSM-IV Text Revision: rationale and potential impact on clinical practice. Psychiatr Serv 2002;53:288-292.
- Jahng S, Na DL, Kang Y. Constructing a composite score for the Seoul Neuropsychological Screening Battery-Core. Dement Neurocogn Disord 2015;14:137-142.
- Xue C, Karjadi C, Paschalidis IC, Au R, Kolachalama VB. Detection of dementia on voice recordings using deep learning: a Framingham Heart Study. Alzheimers Res Ther 2021;13:146.
- ELman JL; JL EL. Effects of frequency-shifted feedback on the pitch of vocal productions. J Acoust Soc Am 1981;70:45-50.
- Jones JA, Munhall KG. Perceptual calibration of F0 production: evidence from feedback perturbation. J Acoust Soc Am 2000;108:1246-1251.
- Houde JF, Jordan MI. Sensorimotor adaptation in speech production. Science 1998;279:1213-1216.
- Pisotta I, Molinari M. Cerebellar contribution to feedforward control of locomotion. Front Hum Neurosci 2014;8:475.
- Houde JF, Nagarajan SS. Speech production as state feedback control. Front Hum Neurosci 2011;5:82.
- Konig A, Satt A, Sorin A, Hoory R, Derreumaux A, David R, et al. Use of speech analyses within a mobile application for the assessment of cognitive impairment in elderly people. Curr Alzheimer Res 2018;15:120-129.
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-444.
- Davis S, Mermelstein P. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust Speech Signal Process 1980;28:357-366.
- Tzanetakis G, Cook P. Musical genre classification of audio signals. IEEE Trans Speech Audio Process 2002;10:293-302.
- Muller M, Ewert S. Chroma toolbox: Matlab implementations for extracting variants of Chroma-based audio features. In: Proceedings of the International Society for Music Information Retrieval Conference; 2011 October 24-28; Miami. [place unknown]: International Society for Music Information Retrieval, 2011.