Login Register Cart 0
Generic placeholder image

当代阿耳茨海默病研究

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe Translate in English
Research Article

时态语音参数检测不同语言的轻度认知障碍:英语和匈牙利语的Speech- gap测试®的验证和比较

卷 19, 期 5, 2022

发表于: 21 June, 2022

页: [373 - 386] 页: 14

弟呕挨: 10.2174/1567205019666220418155130

价格: $65

摘要

背景:自动语音识别(ASR)技术的发展允许分析轻度认知障碍(MCI)的时间(基于时间的)语音参数特征。然而，关于自发语音分析在不同的语言环境中是否能以同样的效率使用，目前还没有相关的资料。 目的:这项国际试点研究的主要目标是解决之前在匈牙利语中测试过的Speech-Gap TestR (S-GAP TestR)是否适合并适用于其他语言(如英语)的MCI识别问题。 方法:在对88个个体进行初步筛选后，基于Petersen的标准，将讲英语(n = 33)和讲匈牙利语(n = 33)的参与者分为MCI或健康对照组(HC)。每个参与者的讲话都通过自发的讲话任务被记录下来。通过ASR确定并计算了15个时间参数。 结果:英语样本中的7个时间参数和匈牙利语样本中的5个时间参数在MCI组和HC组之间有显著差异。受试者工作特征(ROC)分析在语音节奏和发音节奏上有100%的敏感度，在另外三个时间参数上有较高的敏感度(85.7%)，将英语MCI病例与HC组明显区分。在说匈牙利语的样本中，ROC分析显示相似的敏感性(92.3%)。 结论:本研究在不同母语人群中的结果表明，S-GAP TestR检测到的声学参数的变化可能存在于不同语言之间。

关键词: 轻度认知障碍，神经认知障碍，语言，语音分析，时间参数，早期识别。

« Previous Next »
[1]
Braaten AJ, Parsons TD, McCue R, Sellers A, Burns WJ. Neurocognitive differential diagnosis of dementing diseases: Alzheimer’s dementia, vascular dementia, frontotemporal dementia, and major depressive disorder. Int J Neurosci 2006; 116(11): 1271-93.
[http://dx.doi.org/10.1080/00207450600920928] [PMID: 17000529]
[2]
Meilán JJG, Martínez-Sánchez F, Carro J, Sánchez JA, Pérez E. Acoustic markers associated with impairment in language processing in Alzheimer’s Disease. Span J Psychol 2012; 15(2): 487-94.
[http://dx.doi.org/10.5209/rev_SJOP.2012.v15.n2.38859] [PMID: 22774422]
[3]
Forbes KE, Venneri A, Shanks MF. Distinct patterns of spontaneous speech deterioration: An early predictor of Alzheimer’s disease. Brain Cogn 2002; 48(2-3): 356-61.
[http://dx.doi.org/10.1006/brcg.2001.1377] [PMID: 12030467]
[4]
Laske C, Sohrabi HR, Frost SM, et al. Innovative diagnostic tools for early detection of Alzheimer’s disease. Alzheimers Dement 2015; 11(5): 561-78.
[http://dx.doi.org/10.1016/j.jalz.2014.06.004] [PMID: 25443858]
[5]
Taler V, Phillips NA. Language performance in Alzheimer’s disease and mild cognitive impairment: A comparative review. J Clin Exp Neuropsychol 2008; 30(5): 501-56.
[http://dx.doi.org/10.1080/13803390701550128] [PMID: 18569251]
[6]
Ahmed S, Haigh AM, de Jager CA, Garrard P. Connected speech as a marker of disease progression in autopsy-proven Alzheimer’s disease. Brain 2013; 136(Pt 12): 3727-37.
[http://dx.doi.org/10.1093/brain/awt269] [PMID: 24142144]
[7]
Petti U, Baker S, Korhonen A. A systematic literature review of automatic Alzheimer’s disease detection from speech and language. J Am Med Inform Assoc 2020; 27(11): 1784-97.
[http://dx.doi.org/10.1093/jamia/ocaa174] [PMID: 32929494]
[8]
Martínez-Nicolás I, Llorente TE, Martínez-Sánchez F, Meilán JJG. Ten years of research on automatic voice and speech analysis of people with Alzheimer’s disease and mild cognitive impairment: A systematic review article. Front Psychol 2021; 12: 620251.
[http://dx.doi.org/10.3389/fpsyg.2021.620251] [PMID: 33833713]
[9]
Vigo I, Coelho L, Reis S. Speech- and language-based classification of Alzheimer’s disease: A systematic review. Bioengineering (Basel) 2022; 9(1): 27.
[http://dx.doi.org/10.3390/bioengineering9010027] [PMID: 35049736]
[10]
Hoffmann I, Nemeth D, Dye CD, Pákáski M, Irinyi T, Kálmán J. Temporal parameters of spontaneous speech in Alzheimer’s disease. Int J Speech Lang Pathol 2010; 12(1): 29-34.
[http://dx.doi.org/10.3109/17549500903137256] [PMID: 20380247]
[11]
Boersma P. Praat, a system for doing phonetics by computer. Glot Int 2002; 5: 341-5.
[12]
Szatloczki G, Hoffmann I, Vincze V, Kalman J, Pakaski M. Speaking in Alzheimer’s disease, is that an early sign? Importance of changes in language abilities in Alzheimer’s disease. Front Aging Neurosci 2015; 7: 195-202.
[http://dx.doi.org/10.3389/fnagi.2015.00195] [PMID: 26539107]
[13]
Tóth L, Gosztolya G, Vincze V, Hoffmann I, Szatlóczki G, Biró E, et al. Automatic detection of mild cognitive impairment from spontaneous speech using ASR. Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech). 2694-8.
[http://dx.doi.org/10.21437/Interspeech.2015-568]
[14]
Gosztolya G, Tóth L, Grósz T, et al. Detecting mild cognitive impairment from spontaneous speech by correlation-based phonetic feature selection. Proceedings of Interspeech 2016; 107-111: 107-11.
[http://dx.doi.org/10.21437/Interspeech.2016-384]
[15]
Vincze V, Gosztolya G, Tóth L, Hoffmann I, Szatlóczki G, Bánréti Z, et al. Detecting mild cognitive impairment by exploiting linguistic information from transcripts. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL). 181-7.
[http://dx.doi.org/10.18653/v1/P16-2030]
[16]
Tóth L, Hoffmann I, Gosztolya G, et al. A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech. Curr Alzheimer Res 2018; 15(2): 130-8.
[http://dx.doi.org/10.2174/1567205014666171121114930] [PMID: 29165085]
[17]
Gosztolya G, Vincze V, Tóth L, Pákáski M, Kálmán J, Hoffmann I. Identifying mild cognitive impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features. Comput Speech Lang 2019; 53: 181-97.
[http://dx.doi.org/10.1016/j.csl.2018.07.007]
[18]
Solomon A, Mangialasche F, Richard E, et al. Advances in the prevention of Alzheimer’s disease and dementia. J Intern Med 2014; 275(3): 229-50.
[http://dx.doi.org/10.1111/joim.12178] [PMID: 24605807]
[19]
de la Fuente Garcia S, Ritchie CW, Luz S. Artificial intelligence, speech, and language processing approaches to monitoring Alzheimer’s disease: A systematic review. J Alzheimers Dis 2020; 78(4): 1547-74.
[http://dx.doi.org/10.3233/JAD-200888] [PMID: 33185605]
[20]
Chien Y, Hong S, Cheah W, Fu L, Chang Y. An assessment system for Alzheimer’s disease based on speech using a novel feature sequence design and recurrent neural network. IEEE International Conference on Systems, Man, and Cybernetics (SMC). 3289-94.
[http://dx.doi.org/10.1109/SMC.2018.00557]
[21]
Roark B, Mitchell M, Hosom JP, Hollingshead K, Kaye J. Spoken language derived measures for detecting mild cognitive impairment. IEEE Trans Audio Speech Lang Process 2011; 19(7): 2081-90.
[http://dx.doi.org/10.1109/TASL.2011.2112351] [PMID: 22199464]
[22]
Sajjadi SA, Patterson K, Tomek M, Nestor PJ. Abnormalities of connected speech in semantic dementia vs Alzheimer’s disease. Aphasiology 2012; 26(6): 847-66.
[http://dx.doi.org/10.1080/02687038.2012.654933]
[23]
Jarrold W, Peintner B, Wilkins D, Vergryi D, Richey C, Gorno-Tempini ML, et al. Aided diagnosis of dementia type through computer-based analysis of spontaneous speech. Proceedings of the Workshop on Computational Linguistics and Clinical Psychology (CLPsych). 27-37.
[http://dx.doi.org/10.3115/v1/W14-3204]
[24]
De Looze C, Kelly F, Crosby L, et al. Changes in speech chunking in reading aloud is a marker of mild cognitive impairment and mild-to-moderate Alzheimer’s disease. Curr Alzheimer Res 2018; 15(9): 828-47.
[http://dx.doi.org/10.2174/1567205015666180404165017] [PMID: 29623841]
[25]
Luz S, de la Fuente S, Albert P. A method for analysis of patient speech in dialogue for dementia detection. arXiv 2018; 1811.09919.
[26]
Guo Z, Ling Z, Li Y. Detecting Alzheimer’s disease from continuous speech using language models. J Alzheimers Dis 2019; 70(4): 1163-74.
[http://dx.doi.org/10.3233/JAD-190452] [PMID: 31322577]
[27]
Sluis RA, Angus D, Wiles J, et al. An automated approach to examining pausing in the speech of people with dementia. Am J Alzheimers Dis Other Demen 2020; 35: 1533317520939773.
[http://dx.doi.org/10.1177/1533317520939773] [PMID: 32648470]
[28]
König A, Satt A, Sorin A, et al. Automatic speech analysis for the assessment of patients with predementia and Alzheimer’s disease. Alzheimers Dement (Amst) 2015; 1(1): 112-24.
[http://dx.doi.org/10.1016/j.dadm.2014.11.012] [PMID: 27239498]
[29]
König A, Satt A, Sorin A, et al. Use of speech analyses within a mobile application for the assessment of cognitive impairment in elderly people. Curr Alzheimer Res 2018; 15(2): 120-9.
[http://dx.doi.org/10.2174/1567205014666170829111942] [PMID: 28847279]
[30]
Mirzaei S, El Yacoubi M, Garcia-Salicetti S, et al. Two-stage feature selection of voice parameters for early Alzheimer’s disease prediction. IRBM 2018; 39(6): 430-5.
[http://dx.doi.org/10.1016/j.irbm.2018.10.016]
[31]
Tröger J, Linz N, König A, et al. Exploitation vs. exploration-computational temporal and semantic analysis explains semantic verbal fluency impairment in Alzheimer’s disease. Neuropsychologia 2019; 131: 53-61.
[http://dx.doi.org/10.1016/j.neuropsychologia.2019.05.007] [PMID: 31121184]
[32]
Satt A, Sorin A, Toledo-Ronen O, Barkan O, Kompatsiaris I, Kokonozi A, et al. Evaluation of speech-based protocol for detection of early-stage dementia. Proceedings of the Annual Conference of the International Speech Communication Association (Interspeech). 1692-6.
[http://dx.doi.org/10.21437/Interspeech.2013-32]
[33]
Gosztolya G, Balogh R, Imre N, et al. Cross-lingual detection of mild cognitive impairment based on temporal parameters of spontaneous speech. Comput Speech Lang 2021; 69: 101215.
[http://dx.doi.org/10.1016/j.csl.2021.101215]
[34]
Balogh R, Imre N, Gosztolya G, Hoffmann L, Pákáski M, Kálmán J. The role of silence in verbal fluency tasks – a new approach for the detection of mild cognitive impairment. J Int Neuropsychol Soc 2022; •••: 1-13.
[http://dx.doi.org/10.1017/S1355617721001454] [PMID: 35067261]
[35]
Beltrami D, Gagliardi G, Rossini Favretti R, Ghidoni E, Tamburini F, Calzà L. Speech analysis by natural language processing techniques: A possible tool for very early detection of cognitive decline? Front Aging Neurosci 2018; 10: 369.
[http://dx.doi.org/10.3389/fnagi.2018.00369]
[36]
Kato S, Endo H, Homma A, Sakuma T, Watanabe K. Early detection of cognitive impairment in the elderly based on Bayesian mining using speech prosody and cerebral blood flow activation. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 5813-6.
[http://dx.doi.org/10.1109/EMBC.2013.6610873]
[37]
Tanaka H, Adachi H, Ukita N, et al. Detecting dementia through interactive computer avatars. IEEE J Transl Eng Health Med 2017; 5: 2200111.
[http://dx.doi.org/10.1109/JTEHM.2017.2752152] [PMID: 29018636]
[38]
Kobayashi M, Kosugi A, Takagi H, Nemoto M, Nemoto K, Arai T, et al. Effects of age-related cognitive decline on elderly user interactions with voice-based dialogue systems. IFIP Conference on Human-Computer Interaction. 53-74.
[http://dx.doi.org/10.1007/978-3-030-29390-1_4]
[39]
Yamada Y, Shinkawa K, Kobayashi M, et al. Tablet-based automatic assessment for early detection of Alzheimer’s disease using speech responses to daily life questions. Front Digit Health 2021; 3: 653904.
[http://dx.doi.org/10.3389/fdgth.2021.653904] [PMID: 34713127]
[40]
Nasrolahzadeh M, Mohammadpoory Z, Haddadnia J. Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease. Cogn Neurodynamics 2018; 12(6): 583-96.
[http://dx.doi.org/10.1007/s11571-018-9499-8] [PMID: 30483366]
[41]
Meilán JJG, Martínez-Sánchez F, Carro J, López DE, Millian-Morell L, Arana JM. Speech in Alzheimer’s disease: Can temporal and acoustic parameters discriminate dementia? Dement Geriatr Cogn Disord 2014; 37(5-6): 327-34.
[http://dx.doi.org/10.1159/000356726] [PMID: 24481220]
[42]
López-de-Ipiña K, Alonso JB, Travieso CM, et al. On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. Sensors (Basel) 2013; 13(5): 6730-45.
[http://dx.doi.org/10.3390/s130506730] [PMID: 23698268]
[43]
Espinoza-Cuadros F, Garcia-Zamora MA, Torres-Boza D, Ferrer-Riesgo CA, Montero-Benavides A, Gonzales-Moreira E, et al. A spoken language database for research on moderate cognitive impairment: Design and preliminary analysis. Lect Notes Comput Sci 2014; 8854: 219-28. [IberSpeech].
[http://dx.doi.org/10.1007/978-3-319-13623-3_23]
[44]
Gonzalez-Moreira E, Torres-Boza D, Kairuz HA, et al. Automatic prosodic analysis to identify mild dementia. BioMed Res Int 2015; 2015: 916356.
[http://dx.doi.org/10.1155/2015/916356] [PMID: 26558287]
[45]
Fraser KC, Lundholm Fors K, Eckerström M, Öhman F, Kokkinakis D. Predicting MCI status from multimodal language data using cascaded classifiers. Front Aging Neurosci 2019; 11: 205.
[http://dx.doi.org/10.3389/fnagi.2019.00205] [PMID: 31427959]
[46]
Themistocleous C, Eckerström M, Kokkinakis D. Voice quality and speech fluency distinguish individuals with Mild Cognitive Impairment from Healthy Controls. PLoS One 2020; 15(7): e0236009.
[http://dx.doi.org/10.1371/journal.pone.0236009] [PMID: 32658934]
[47]
Khodabakhsh A, Demiroglu C. Analysis of speech-based measures for detecting and monitoring Alzheimer’s disease. Methods Mol Biol 2015; 1246: 159-73.
[http://dx.doi.org/10.1007/978-1-4939-1985-7_11] [PMID: 25417086]
[48]
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: Clinical characterization and outcome. Arch Neurol 1999; 56(3): 303-8.
[http://dx.doi.org/10.1001/archneur.56.3.303] [PMID: 10190820]
[49]
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12(3): 189-98.
[http://dx.doi.org/10.1016/0022-3956(75)90026-6] [PMID: 1202204]
[50]
Manos PJ, Wu R. The ten point clock test: A quick screen and grading method for cognitive impairment in medical and surgical patients. Int J Psychiatry Med 1994; 24(3): 229-44.
[http://dx.doi.org/10.2190/5A0F-936P-VG8N-0F5R] [PMID: 7890481]
[51]
Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: A preliminary report. J Psychiatr Res 1982-1983; 17(1): 37-49.
[http://dx.doi.org/10.1016/0022-3956(82)90033-4] [PMID: 7183759]
[52]
Sheikh JI, Yesavage JA. Geriatric Depression Scale (GDS) – Recent evidence and development of a shorter version. Clin Gerontol 1986; 5: 165-73.
[http://dx.doi.org/10.1300/J018v05n01_09]
[53]
Young S, Evermann G, Gales MJF, Hain T, Kershaw D, Liu X, et al. The HTK Book. Cambridge, UK: Cambridge University 2006.
[54]
Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process Mag 2012; 29(6): 82-97.
[http://dx.doi.org/10.1109/MSP.2012.2205597]
[55]
Tóth L. Phone recognition with hierarchical convolutional deep maxout networks. EURASIP J Audio Speech Music Process 2015; 25(1): 1-3.
[http://dx.doi.org/10.1186/s13636-015-0068-3]
[56]
Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS). 315-23.
[57]
Rousseau A, Delglise P, Estve Y. TED-LIUM: An automatic speech recognition dedicated corpus. Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12). 125-9.
[58]
Neuberger T, Gyarmathy D, Gráczi TE, Horváth V, Gósy M, Beke A. Development of a large spontaneous speech database of agglutinative Hungarian language. Text Speech and Dialogue 2014; pp. 424-31.
[http://dx.doi.org/10.1007/978-3-319-10816-2_51]
[59]
Eklund R. Prolongations: A dark horse in the disfluency stable. Proceedings of Disfluency in Spontaneous Speech (DiSS) 2001; 5-8.
[60]
Deme A, Markó A. Lengthenings and filled pauses in Hungarian adults' and children's speech. Proceedings of Disfluency in Spontaneous Speech (DiSS) 2013; 21-4.
[61]
Gayraud F, Lee HR, Barkat-Defradas M. Syntactic and lexical context of pauses and hesitations in the discourse of Alzheimer patients and healthy elderly subjects. Clin Linguist Phon 2011; 25(3): 198-209.
[http://dx.doi.org/10.3109/02699206.2010.521612] [PMID: 21080826]
[62]
Lee H, Gayraud F, Hirsh F, Barkat-Defradas M. Speech dysfluencies in normal and pathological aging: A comparison between Alzheimer patients and healthy elderly subjects. Proceedings of the 17th International Congress of Phonetic Sciences (ICPhS). 1174-7.
[63]
Singh S, Bucks RS, Cuerden JM. Evaluation of an objective technique for analysing temporal variables in DAT spontaneous speech. Aphasiology 2001; 15(6): 571-83.
[http://dx.doi.org/10.1080/02687040143000041]
[64]
Sarazin M, Chauviré V, Gerardin E, et al. The amnestic syndrome of hippocampal type in Alzheimer’s disease: An MRI study. J Alzheimers Dis 2010; 22(1): 285-94.
[http://dx.doi.org/10.3233/JAD-2010-091150] [PMID: 20847406]
[65]
Pistono A, Jucla M, Barbeau EJ, et al. Pauses during autobiographical discourse reflect episodic memory processes in early Alzheimer’s disease. J Alzheimers Dis 2016; 50(3): 687-98.
[http://dx.doi.org/10.3233/JAD-150408] [PMID: 26757034]
[66]
Simons JS, Gilbert SJ, Owen AM, Fletcher PC, Burgess PW. Distinct roles for lateral and medial anterior prefrontal cortex in contextual recollection. J Neurophysiol 2005; 94(1): 813-20.
[http://dx.doi.org/10.1152/jn.01200.2004] [PMID: 15728761]
[67]
Roca M, Torralva T, Gleichgerrcht E, et al. The role of Area 10 (BA10) in human multitasking and in social cognition: A lesion study. Neuropsychologia 2011; 49(13): 3525-31.
[http://dx.doi.org/10.1016/j.neuropsychologia.2011.09.003] [PMID: 21930138]
[68]
Arlt S, Buchert R, Spies L, Eichenlaub M, Lehmbeck JT, Jahn H. Association between fully automated MRI-based volumetry of different brain regions and neuropsychological test performance in patients with amnestic mild cognitive impairment and Alzheimer’s disease. Eur Arch Psychiatry Clin Neurosci 2013; 263(4): 335-44.
[http://dx.doi.org/10.1007/s00406-012-0350-7] [PMID: 22940716]
[69]
Wilson SM, Henry ML, Besbris M, et al. Connected speech production in three variants of primary progressive aphasia. Brain 2010; 133(Pt 7): 2069-88.
[http://dx.doi.org/10.1093/brain/awq129] [PMID: 20542982]
[70]
Hernández-Domínguez L, Ratté S, Sierra-Martínez G, Roche-Bergua A. Computer-based evaluation of Alzheimer’s disease and mild cognitive impairment patients during a picture description task. Alzheimers Dement (Amst) 2018; 10(1): 260-8.
[http://dx.doi.org/10.1016/j.dadm.2018.02.004] [PMID: 29780871]
[71]
Gauthier S, Reisberg B, Zaudig M, et al. Mild cognitive impairment. Lancet 2006; 367(9518): 1262-70.
[http://dx.doi.org/10.1016/S0140-6736(06)68542-5] [PMID: 16631882]

Rights & Permissions Print Cite
Article Metrics
42
1
© 2024 Bentham Science Publishers | Privacy Policy