Organization WH. A blueprint for dementia research. Geneva: World Health Organization; 2022.

Google Scholar 

Nichols E, Steinmetz JD, Vollset SE, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Pub Health. 2022;7(2):105–25. https://doi.org/10.1016/S2468-2667(21)00249-8.

Article  Google Scholar 

Jia X, Wang Z, Huang F, et al. A comparison of the mini-mental state examination (MMSE) with the Montreal cognitive assessment (MoCA) for mild cognitive impairment screening in Chinese middle-aged and older population: a cross-sectional study. BMC Psychiatr. 2021;21:1–13. https://doi.org/10.1186/s12888-021-03495-6.

Article  Google Scholar 

Pinto TCC, Machado L, Bulgacov TM, et al. Is the Montreal cognitive assessment (MoCA) screening superior to the mini-mental state examination (MMSE) in the detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD) in the elderly? Int Psychogeriatr. 2019;31(4):491–504. https://doi.org/10.1017/S1041610218001370.

Article  Google Scholar 

Leng Y, Cui W, Peng Y, et al. Multimodal cross enhanced fusion network for diagnosis of Alzheimer’s disease and subjective memory complaints. Comput Biol Med. 2023;157:106788. https://doi.org/10.1016/j.compbiomed.2023.106788.

Article  Google Scholar 

Kaplan E, Baygin M, Barua PD, et al. ExHiF: Alzheimer’s disease detection using exemplar histogram-based features with CT and MR images. Med Eng Phys. 2023;115:103971. https://doi.org/10.1016/j.medengphy.2023.103971.

Article  Google Scholar 

Hajjar I, Okafor M, Choi JD, et al. Development of digital voice biomarkers and associations with cognition, cerebrospinal biomarkers, and neural representation in early Alzheimer’s disease. Alzheimer’s Dement: Diagn Assess Dis Monitor. 2023;15(1):12393. https://doi.org/10.1002/dad2.12393.

Article  MATH  Google Scholar 

Mestach M, Hartsuiker RJ, Pistono A. Can we track the progression of Alzheimer’s disease via lexical-semantic variables in connected speech? J Neurolinguist. 2024;70:101189. https://doi.org/10.1016/j.jneuroling.2023.101189.

Article  Google Scholar 

Chen X, Pu Y, Li J, et al. Cross-lingual Alzheimer’s disease detection based on paralinguistic and pre-trained features In: ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, 2023; pp. 1–2. https://doi.org/10.1109/ICASSP49357.2023.10095522.

Tripathi T, Kumar R. Speech-based detection of multi-class Alzheimer’s disease classification using machine learning. Int J Data Sci Anal. 2024;18(1):83–96.

Article  MATH  Google Scholar 

Chen J, Ye J, Tang F, et al. Automatic detection of Alzheimer’s disease using spontaneous speech only. In: Interspeech 2021. 2021; pp: 3830–3834. https://doi.org/10.21437/Interspeech.2021-2002.

Kumar MR, Vekkot S, Lalitha S, et al. Dementia detection from speech using machine learning and deep learning architectures. Sensors. 2022;22(23):9311. https://doi.org/10.3390/s22239311.

Article  MATH  Google Scholar 

Agbavor F, Liang H. Artificial intelligence-enabled end-to-end detection and assessment of Alzheimer’s disease using voice. Brain Sci. 2023. https://doi.org/10.3390/brainsci13010028.

Article  Google Scholar 

Jin L, Oh Y, Kim H, et al. Consen: complementary and simultaneous ensemble for Alzheimer’s disease detection and MMSE score prediction. In: ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, 2023; pp. 1–2. https://doi.org/10.1109/ICASSP49357.2023.10096253.

Ilias L, Askounis D, Psarras J. Detecting dementia from speech and transcripts using transformers. Comput Speech Lang. 2023;79:101485. https://doi.org/10.1016/j.csl.2023.101485.

Article  MATH  Google Scholar 

Meghanani A, Anoop CS, Ramakrishnan AG. An exploration of log-mel spectrogram and MFCC features for Alzheimer’s dementia recognition from spontaneous speech. In: 2021 IEEE spoken language technology workshop (SLT). 2021; pp. 670–677. https://doi.org/10.1109/SLT48900.2021.9383491.

Pacheco-Lorenzo MR, Christensen H, Anido-Rifón LE, et al. Analysis of voice biomarkers for the detection of cognitive impairment. IEEE Access. 2024;12:122840–51. https://doi.org/10.1109/ACCESS.2024.3442431.

Article  MATH  Google Scholar 

Warule P, Mishra SP, Deb S. Time-frequency analysis of speech signal using Chirplet transform for automatic diagnosis of Parkinson’s disease. Biomed Eng Lett. 2023;13(4):613–23.

Article  MATH  Google Scholar 

Kasture N, Jain P. Automatic recognition of disordered children’s speech signal in dyadic interaction using deep learning models. Multimed Tools Appl. 2024;83(16):49493–513. https://doi.org/10.1007/s11042-023-17461-9.

Article  MATH  Google Scholar 

Salvati D, Drioli C, Foresti GL. A late fusion deep neural network for robust speaker identification using raw waveforms and gammatone cepstral coefficients. Expert Syst Appl. 2023;222:119750. https://doi.org/10.1016/j.eswa.2023.119750.

Article  Google Scholar 

Meng W, Zhang Q, Ma S, et al. A lightweight CNN and transformer hybrid model for mental retardation screening among children from spontaneous speech. Comput Biol Med. 2022;151:106281. https://doi.org/10.1016/j.compbiomed.2022.106281.

Article  MATH  Google Scholar 

Ivanova O, Martínez-Nicolás I, Meilán JJG. Speech changes in old age: methodological considerations for speech-based discrimination of healthy ageing and Alzheimer’s disease. Int J Lang Commun Disord. 2024;59(1):13–37. https://doi.org/10.1111/1460-6984.12888.

Article  Google Scholar 

Li Y, Lai C, Lala D, et al. Alzheimer’s dementia detection through spontaneous dialogue with proactive robotic listeners. In: 2022 17th ACM/IEEE international conference on human–robot interaction (HRI), IEEE, 2022; pp. 875–879. https://doi.org/10.1109/HRI53351.2022.9889375.

Tamm B, Vandenberghe R, Van Hamme H. Cross-lingual transfer learning for Alzheimer’s detection from spontaneous speech. In: ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, 2023; pp. 1–2. https://doi.org/10.1109/ICASSP49357.2023.10096770.

Nishikawa K, Akihiro K, Hirakawa R, et al. Machine learning model for discrimination of mild dementia patients using acoustic features. Cognit Robot. 2022;2:21–9. https://doi.org/10.1016/j.cogr.2021.12.003.

Article  MATH  Google Scholar 

Suman S, Sahoo K S, Das C, et al. Visualization of audio files using librosa. In: Proceedings of 2nd international conference on mathematical modeling and computational science: ICMMCS 2021. 2022; pp. 409–418.

Alex A, Wang L, Gastaldo P, et al. Data augmentation for speech separation. Speech Commun. 2023;152:102949. https://doi.org/10.1016/j.specom.2023.05.009.

Article  MATH  Google Scholar 

Tan M, Le Q. Efficientnetv2: smaller models and faster training. In: International conference on machine learning, PMLR, 2021; pp. 10096–10106. https://doi.org/10.48550/arXiv.2104.00298.

Shams AM, Jabbari S. A deep learning approach for diagnosis of schizophrenia disorder via data augmentation based on convolutional neural network and long short-term memory. Biomed Eng Lett. 2024. https://doi.org/10.1007/s13534-024-00360-9.

Article  MATH  Google Scholar 

Burke E, Gunstad J, Pavlenko O, et al. Distinguishable features of spontaneous speech in Alzheimer’s clinical syndrome and healthy controls. Aging Neuropsychol Cognit. 2024;31(3):575–86. https://doi.org/10.1080/13825585.2023.2221020.

Article  MATH  Google Scholar 

Liu J, Fu F, Li L, et al. Efficient pause extraction and encode strategy for Alzheimer’s disease detection using only acoustic features from spontaneous speech. Brain Sci. 2023;13(3):477. https://doi.org/10.3390/brainsci13030477.

Article  MATH  Google Scholar