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B.T B, Chen JM. Performance Assessment of ChatGPT versus Bard in Detecting Alzheimer's Dementia. Diagnostics (Basel) 2024; 14:817. [PMID: 38667463 PMCID: PMC11048951 DOI: 10.3390/diagnostics14080817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
Large language models (LLMs) find increasing applications in many fields. Here, three LLM chatbots (ChatGPT-3.5, ChatGPT-4, and Bard) are assessed in their current form, as publicly available, for their ability to recognize Alzheimer's dementia (AD) and Cognitively Normal (CN) individuals using textual input derived from spontaneous speech recordings. A zero-shot learning approach is used at two levels of independent queries, with the second query (chain-of-thought prompting) eliciting more detailed information than the first. Each LLM chatbot's performance is evaluated on the prediction generated in terms of accuracy, sensitivity, specificity, precision, and F1 score. LLM chatbots generated a three-class outcome ("AD", "CN", or "Unsure"). When positively identifying AD, Bard produced the highest true-positives (89% recall) and highest F1 score (71%), but tended to misidentify CN as AD, with high confidence (low "Unsure" rates); for positively identifying CN, GPT-4 resulted in the highest true-negatives at 56% and highest F1 score (62%), adopting a diplomatic stance (moderate "Unsure" rates). Overall, the three LLM chatbots can identify AD vs. CN, surpassing chance-levels, but do not currently satisfy the requirements for clinical application.
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Affiliation(s)
- Balamurali B.T
- Science, Mathematics & Technology (SMT), Singapore University of Technology & Design, 8 Somapah Rd, Singapore 487372, Singapore
| | - Jer-Ming Chen
- Science, Mathematics & Technology (SMT), Singapore University of Technology & Design, 8 Somapah Rd, Singapore 487372, Singapore
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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. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:13-37. [PMID: 37140204 DOI: 10.1111/1460-6984.12888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 04/03/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND Recent evidence suggests that speech substantially changes in ageing. As a complex neurophysiological process, it can accurately reflect changes in the motor and cognitive systems underpinning human speech. Since healthy ageing is not always easily discriminable from early stages of dementia based on cognitive and behavioural hallmarks, speech is explored as a preclinical biomarker of pathological itineraries in old age. A greater and more specific impairment of neuromuscular activation, as well as a specific cognitive and linguistic impairment in dementia, unchain discriminating changes in speech. Yet, there is no consensus on such discriminatory speech parameters, neither on how they should be elicited and assessed. AIMS To provide a state-of-the-art on speech parameters that allow for early discrimination between healthy and pathological ageing; the aetiology of these parameters; the effect of the type of experimental stimuli on speech elicitation and the predictive power of different speech parameters; and the most promising methods for speech analysis and their clinical implications. METHODS & PROCEDURES A scoping review methodology is used in accordance with the PRISMA model. Following a systematic search of PubMed, PsycINFO and CINAHL, 24 studies are included and analysed in the review. MAIN CONTRIBUTION The results of this review yield three key questions for the clinical assessment of speech in ageing. First, acoustic and temporal parameters are more sensitive to changes in pathological ageing and, of these two, temporal variables are more affected by cognitive impairment. Second, different types of stimuli can trigger speech parameters with different degree of accuracy for the discrimination of clinical groups. Tasks with higher cognitive load are more precise in eliciting higher levels of accuracy. Finally, automatic speech analysis for the discrimination of healthy and pathological ageing should be improved for both research and clinical practice. CONCLUSIONS & IMPLICATIONS Speech analysis is a promising non-invasive tool for the preclinical screening of healthy and pathological ageing. The main current challenges of speech analysis in ageing are the automatization of its clinical assessment and the consideration of the speaker's cognitive background during evaluation. WHAT THIS PAPER ADDS What is already known on the subject Societal aging goes hand in hand with the rising incidence of ageing-related neurodegenerations, mainly Alzheimer's disease (AD). This is particularly noteworthy in countries with longer life expectancies. Healthy ageing and early stages of AD share a set of cognitive and behavioural characteristics. Since there is no cure for dementias, developing methods for accurate discrimination of healthy ageing and early AD is currently a priority. Speech has been described as one of the most significantly impaired features in AD. Neuropathological alterations in motor and cognitive systems would underlie specific speech impairment in dementia. Since speech can be evaluated quickly, non-invasively and inexpensively, its value for the clinical assessment of ageing itineraries may be particularly high. What this paper adds to existing knowledge Theoretical and experimental advances in the assessment of speech as a marker of AD have developed rapidly over the last decade. Yet, they are not always known to clinicians. Furthermore, there is a need to provide an updated state-of-the-art on which speech features are discriminatory to AD, how they can be assessed, what kind of results they can yield, and how such results should be interpreted. This article provides an updated overview of speech profiling, methods of speech measurement and analysis, and the clinical power of speech assessment for early discrimination of AD as the most common cause of dementia. What are the potential or actual clinical implications of this work? This article provides an overview of the predictive potential of different speech parameters in relation to AD cognitive impairment. In addition, it discusses the effect that the cognitive state, the type of elicitation task and the type of assessment method may have on the results of the speech-based analysis in ageing.
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Affiliation(s)
- Olga Ivanova
- Spanish Language Department, Faculty of Philology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| | - Israel Martínez-Nicolás
- Department of Basic Psychology, Psychobiology and Behavioral Science Methodology, Faculty of Psychology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| | - Juan José García Meilán
- Department of Basic Psychology, Psychobiology and Behavioral Science Methodology, Faculty of Psychology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
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Parsapoor M. AI-based assessments of speech and language impairments in dementia. Alzheimers Dement 2023; 19:4675-4687. [PMID: 37578167 DOI: 10.1002/alz.13395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 08/15/2023]
Abstract
Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI-powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.
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Affiliation(s)
- Mahboobeh Parsapoor
- Centre de Recherche Informatique de Montréal: CRIM, Montreal, Quebec, Canada
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Chandler C, Diaz‐Asper C, Turner RS, Reynolds B, Elvevåg B. An explainable machine learning model of cognitive decline derived from speech. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12516. [PMID: 38155915 PMCID: PMC10752754 DOI: 10.1002/dad2.12516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023]
Abstract
INTRODUCTION Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders. METHODS Speech collected over the telephone from 91 older participants who were cognitively healthy (n = 29) or had diagnoses of AD (n = 30) or amnestic MCI (aMCI; n = 32) was analyzed with multimodal natural language and speech processing methods. An explainable ensemble decision tree classifier for the multiclass prediction of cognitive decline was created. RESULTS This approach was 75% accurate overall-an improvement over traditional speech-based screening tools and a unimodal language-based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data. DISCUSSION This work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies. Highlights Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening.Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls.AI predictions of cognitive decline are more accurate than experts and standard tools.The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.
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Affiliation(s)
- Chelsea Chandler
- Institute of Cognitive ScienceUniversity of ColoradoBoulderColoradoUSA
| | | | - Raymond S. Turner
- Department of NeurologyGeorgetown UniversityWashingtonDistrict of ColumbiaUSA
| | - Brigid Reynolds
- Department of NeurologyGeorgetown UniversityWashingtonDistrict of ColumbiaUSA
| | - Brita Elvevåg
- Department of Clinical MedicineUniversity of Tromsø – the Arctic University of NorwayTromsøNorway
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Svindt V, Gosztolya G, Gráczi TE. Narrative recall in relapsing-remitting multiple sclerosis: A potentially useful speech task for detecting subtle cognitive changes. CLINICAL LINGUISTICS & PHONETICS 2023; 37:549-566. [PMID: 36715451 DOI: 10.1080/02699206.2023.2170830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 05/20/2023]
Abstract
Our research studied relapsing-remitting multiple sclerosis (RRMS). In half of the RRMS cases, mild cognitive difficulties are present, but often remain undetected despite their adverse effects on individuals' daily life. Detecting subtle cognitive alterations using speech analysis have rarely been implemented in MS research. We applied automatic speech recognition technology to devise a speech task with potential diagnostic value. Therefore, we used two narrative tasks adjusted for the neural and cognitive characteristics of RRMS; namely narrative recall and personal narrative. In addition to speech analysis, we examined the information processing speed, working memory, verbal fluency, and naming skills. Twenty-one participants with RRMS and 21 gender-, age-, and education-matched healthy controls took part in the study. All the participants with RRMS achieved a normal performance on Addenbrooke's Cognitive Examination. The following parameters of speech were measured: articulation and speech rate, the proportion, duration, frequency, and average length of silent and filled pauses. We found significant differences in the temporal parameters between groups and speech tasks. ROC analysis produced high classification accuracy for the narrative recall task (0.877 and 0.866), but low accuracy for the personal narrative task (0.617 and 0.592). The information processing speed affected the speech of the RRMS group but not that of the control group. The higher cognitive load of the narrative recall task may be the cause of significant changes in the speech of the RRMS group relative to the controls. Results suggest that narrative recall task may be effective for detecting subtle cognitive changes in RRMS.
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Affiliation(s)
- Veronika Svindt
- Research Centre for Linguistics, Eötvös Loránd Research Network, Budapest, Hungary
| | - Gábor Gosztolya
- Eötvös Lorand Research Network - University of Szeged, Research Group on Artificial Intelligence, Szeged, Hungary
| | - Tekla E Gráczi
- Research Centre for Linguistics, Eötvös Loránd Research Network, Budapest, Hungary
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Bae M, Seo MG, Ko H, Ham H, Kim KY, Lee JY. The efficacy of memory load on speech-based detection of Alzheimer's disease. Front Aging Neurosci 2023; 15:1186786. [PMID: 37333455 PMCID: PMC10272350 DOI: 10.3389/fnagi.2023.1186786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/16/2023] [Indexed: 06/20/2023] Open
Abstract
Introduction The study aims to test whether an increase in memory load could improve the efficacy in detection of Alzheimer's disease and prediction of the Mini-Mental State Examination (MMSE) score. Methods Speech from 45 mild-to-moderate Alzheimer's disease patients and 44 healthy older adults were collected using three speech tasks with varying memory loads. We investigated and compared speech characteristics of Alzheimer's disease across speech tasks to examine the effect of memory load on speech characteristics. Finally, we built Alzheimer's disease classification models and MMSE prediction models to assess the diagnostic value of speech tasks. Results The speech characteristics of Alzheimer's disease in pitch, loudness, and speech rate were observed and the high-memory-load task intensified such characteristics. The high-memory-load task outperformed in AD classification with an accuracy of 81.4% and MMSE prediction with a mean absolute error of 4.62. Discussion The high-memory-load recall task is an effective method for speech-based Alzheimer's disease detection.
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Affiliation(s)
- Minju Bae
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Myo-Gyeong Seo
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyunwoong Ko
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
- Samsung Medical Center, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyunsun Ham
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
| | - Keun You Kim
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jun-Young Lee
- Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
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Petti U, Baker S, Korhonen A, Robin J. The Generalizability of Longitudinal Changes in Speech Before Alzheimer's Disease Diagnosis. J Alzheimers Dis 2023; 92:547-564. [PMID: 36776053 DOI: 10.3233/jad-220847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
BACKGROUND Language impairment in Alzheimer's disease (AD) has been widely studied but due to limited data availability, relatively few studies have focused on the longitudinal change in language in the individuals who later develop AD. Significant differences in speech have previously been found by comparing the press conference transcripts of President Bush and President Reagan, who was later diagnosed with AD. OBJECTIVE In the current study, we explored whether the patterns previously established in the single AD-healthy control (HC) participant pair apply to a larger group of individuals who later receive AD diagnosis. METHODS We replicated previous methods on two larger corpora of longitudinal spontaneous speech samples of public figures, consisting of 10 and 9 AD-HC participant pairs. As we failed to find generalizable patterns of language change using previous methodology, we proposed alternative methods for data analysis, investigating the benefits of using different language features and their change with age, and compiling the single features into aggregate scores. RESULTS The single features that showed the strongest results were moving average type:token ratio (MATTR) and pronoun-related features. The aggregate scores performed better than the single features, with lexical diversity capturing a similar change in two-thirds of the participants. CONCLUSION Capturing universal patterns of language change prior to AD can be challenging, but the decline in lexical diversity and changes in MATTR and pronoun-related features act as promising measures that reflect the cognitive changes in many participants.
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Affiliation(s)
- Ulla Petti
- University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Simon Baker
- University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Anna Korhonen
- University of Cambridge, Language Technology Lab, Cambridge, UK
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8
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Hajjar I, Okafor M, Choi JD, Moore E, Abrol A, Calhoun VD, Goldstein FC. Development of digital voice biomarkers and associations with cognition, cerebrospinal biomarkers, and neural representation in early Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12393. [PMID: 36777093 PMCID: PMC9899764 DOI: 10.1002/dad2.12393] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 02/08/2023]
Abstract
Introduction Advances in natural language processing (NLP), speech recognition, and machine learning (ML) allow the exploration of linguistic and acoustic changes previously difficult to measure. We developed processes for deriving lexical-semantic and acoustic measures as Alzheimer's disease (AD) digital voice biomarkers. Methods We collected connected speech, neuropsychological, neuroimaging, and cerebrospinal fluid (CSF) AD biomarker data from 92 cognitively unimpaired (40 Aβ+) and 114 impaired (63 Aβ+) participants. Acoustic and lexical-semantic features were derived from audio recordings using ML approaches. Results Lexical-semantic (area under the curve [AUC] = 0.80) and acoustic (AUC = 0.77) scores demonstrated higher diagnostic performance for detecting MCI compared to Boston Naming Test (AUC = 0.66). Only lexical-semantic scores detected amyloid-β status (p = 0.0003). Acoustic scores associated with hippocampal volume (p = 0.017) while lexical-semantic scores associated with CSF amyloid-β (p = 0.007). Both measures were significantly associated with 2-year disease progression. Discussion These preliminary findings suggest that derived digital biomarkers may identify cognitive impairment in preclinical and prodromal AD, and may predict disease progression. Highlights This study derived lexical-semantic and acoustics features as Alzheimer's disease (AD) digital biomarkers.These features were derived from audio recordings using machine learning approaches.Voice biomarkers detected cognitive impairment and amyloid-β status in early stages of AD.Voice biomarkers may predict Alzheimer's disease progression.These markers significantly mapped to functional connectivity in AD-susceptible brain regions.
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Affiliation(s)
- Ihab Hajjar
- Department of NeurologyUniversity of Texas SouthwesternDallasTexasUSA,Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Maureen Okafor
- Department of NeurologyEmory University School of MedicineAtlantaGeorgiaUSA
| | - Jinho D. Choi
- Department of Computer ScienceEmory UniversityAtlantaGeorgiaUSA
| | - Elliot Moore
- School of Electrical & Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Anees Abrol
- Tri‐institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State UniversityGeorgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State UniversityGeorgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
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Mahon E, Lachman ME. Voice biomarkers as indicators of cognitive changes in middle and later adulthood. Neurobiol Aging 2022; 119:22-35. [PMID: 35964541 PMCID: PMC9487188 DOI: 10.1016/j.neurobiolaging.2022.06.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 06/20/2022] [Accepted: 06/26/2022] [Indexed: 11/20/2022]
Abstract
Voice prosody measures have been linked with Alzheimer's disease (AD), but it is unclear whether they are associated with normal cognitive aging. We assessed relationships between voice measures and 10-year cognitive changes in the MIDUS national sample of middle-aged and older adults ages 42-92, with a mean age of 64.09 (standard deviation = 11.23) at the second wave. Seven cognitive tests were assessed in 2003-2004 (Wave 2) and 2013-2014 (Wave 3). Voice measures were collected at Wave 3 (N = 2585) from audio recordings of the cognitive interviews. Analyses controlled for age, education, depressive symptoms, and health. As predicted, higher jitter was associated with greater declines in episodic memory, verbal fluency, and attention switching. Lower pulse was related to greater decline in episodic memory, and fewer voice breaks were related to greater declines in episodic memory and verbal fluency, although the direction of these effects was contrary to hypotheses. Findings suggest that voice biomarkers may offer a promising approach for early detection of risk factors for cognitive impairment or AD.
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Affiliation(s)
- Elizabeth Mahon
- Brandeis University, Department of Psychology, Waltham, MA, USA.
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10
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Kálmán J, Devanand DP, Gosztolya G, Balogh R, Imre N, Tóth L, Hoffmann I, Kovács I, Vincze V, Pákáski M. Temporal speech parameters detect mild cognitive impairment in different languages: validation and comparison of the Speech-GAP Test® in English and Hungarian. Curr Alzheimer Res 2022; 19:373-386. [PMID: 35440309 DOI: 10.2174/1567205019666220418155130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/08/2022] [Accepted: 02/17/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND The development of automatic speech recognition (ASR) technology allows the analysis of temporal (time-based) speech parameters characteristic of mild cognitive impairment (MCI). However, no information has been available on whether the analysis of spontaneous speech can be used with the same efficiency in different language environments. OBJECTIVE The main goal of this international pilot study is to address the question whether the Speech-Gap Test® (S-GAP Test®), previously tested in the Hungarian language, is appropriate for and applicable to the recognition of MCI in other languages such as English. METHOD After an initial screening of 88 individuals, English-speaking (n = 33) and Hungarian-speaking (n = 33) participants were classified as having MCI or as healthy controls (HC) based on Petersen's criteria. Speech of each participant was recorded via a spontaneous speech task. 15 temporal parameters were determined and calculated by means of ASR. RESULTS Seven temporal parameters in the English-speaking sample and 5 in the Hungarian-speaking sample showed significant differences between the MCI and the HC group. Receiver operating characteristics (ROC) analysis clearly distinguished the English-speaking MCI cases from the HC group based on speech tempo and articulation tempo with 100% sensitivity, and on three more temporal parameters with high sensitivity (85.7%). In the Hungarian-speaking sample, the ROC analysis showed similar sensitivity rates (92.3%). CONCLUSION The results of this study in different native-speaking populations suggest that changes in acoustic parameters detected by the S-GAP Test® might be present across different languages.
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Affiliation(s)
- János Kálmán
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Davangere P Devanand
- Columbia University Medical Center, New York, NY.,New York State Psychiatric Institute, New York, NY
| | - Gábor Gosztolya
- MTA-SZTE Research Group on Artificial Intelligence, Faculty of Science and Informatics, University of Szeged, Szeged
| | - Réka Balogh
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Nóra Imre
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - László Tóth
- Faculty of Science and Informatics, University of Szeged, Szeged
| | - Ildikó Hoffmann
- Faculty of Humanities and Social Sciences, University of Szeged, Szeged.,Hungarian Research Centre for Linguistics, Eötvös Loránd Research Network, Budapest
| | - Ildikó Kovács
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
| | - Veronika Vincze
- MTA-SZTE Research Group on Artificial Intelligence, Faculty of Science and Informatics, University of Szeged, Szeged
| | - Magdolna Pákáski
- Albert Szent-Györgyi Medical School, University of Szeged, Szeged
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Bertini F, Allevi D, Lutero G, Calzà L, Montesi D. An automatic Alzheimer’s disease classifier based on spontaneous spoken English. COMPUT SPEECH LANG 2022. [DOI: 10.1016/j.csl.2021.101298] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Munthuli A, Vongsurakrai S, Anansiripinyo T, Ellermann V, Sroykhumpa K, Onsuwan C, Chutichetpong P, Hemrungrojn S, Kosawat K, Tantibundhit C. Thammasat-NECTEC-Chula's Thai Language and Cognition Assessment (TLCA): The Thai Alzheimer's and Mild Cognitive Impairment Screening Test. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:690-694. [PMID: 34891386 DOI: 10.1109/embc46164.2021.9630779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Thammasat-NECTEC-Chula's Thai Language and Cognition Assessment (TLCA) is a cognitive paper-based test consisting of 21 tasks that cover 3 domains: memory, language, and other cognitive abilities. The TLCA follows some aspects of the existing tests (Thai Addenbrooke's Cognitive Examination-Revised (Thai-ACE-R) and the Thai Montreal Cognitive Assessment Test (Thai-MoCA)) and many parts were reconstructed to be more adapted to the Thai culture. Data obtained from the test will be able to precisely distinguish between patients with Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), and Normal healthy Controls (NC). The TLCA was tested on 90 participants (32 on the paper-based version and 58 on the computerized version) using a scoring procedure and speech features from verbal responses with machine learning classification. The scoring results showed significant difference between non-AD (NC + MCI) vs AD participants in 3 domains and could differentiate between NC and MCI, while machine classification could classify in three settings: NC vs non-NC (MCI + AD), AD vs non-AD and NC vs MCI vs AD. These promising results suggest that TLCA could be further verified and used as an efficient assessment in MCI and AD screening for Thais.Clinical relevance- The speech feature analysis of TLCA showed promising result for screening MCI and AD for Thais.
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Oh C, Morris RJ, Wang X. A Systematic Review of Expressive and Receptive Prosody in People With Dementia. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2021; 64:3803-3825. [PMID: 34529922 DOI: 10.1044/2021_jslhr-21-00013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Purpose This review was designed to provide a systematic overview of prosody in people with a primary diagnosis of dementia (PwD) and evaluate the potential use of prosodic features for diagnosis of dementia. Method A systematic search of five databases was conducted using Medical Subject Headings and keywords. Studies included in the review were evaluated for their methodological quality using the modified Joanna Briggs Institute checklist. Results A total of 14 articles were identified as being relevant for this review. Among the 14 articles, the methodological quality ranged, with eight rated as weak, four rated as moderate, and two rated as strong. Ten of the 14 articles had people with Alzheimer's disease (AD) as participants, and the remaining four had people with frontotemporal dementia as participants. Four articles focused on receptive prosody, another six focused on expressive prosody, and the remaining four articles were investigations into both. The 14 articles presented inconsistent findings, and various tasks were used to measure prosodic features in PwD in the articles. Prosody was studied as a diagnostic tool for dementia in four of the articles, all of which were based on expressive prosody in individuals with AD. Among the four articles, three proposed the use of automatic speech analysis for diagnosis of AD. Conclusions This review demonstrates that prosody in PwD is an underinvestigated area. In particular, it was concerning that most articles were of weak methodological quality. Nevertheless, it was found that prosody may be a potential diagnostic tool for assessing dementia. More studies that replicate the existing studies and those with stronger methodology are needed to confirm that receptive and/or expressive prosody can be used for dementia diagnosis.
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Affiliation(s)
- Chorong Oh
- School of Rehabilitation and Communication Sciences, Ohio University, Athens
| | - Richard J Morris
- School of Communication Science & Disorders, Florida State University, Tallahassee
| | - Xianhui Wang
- School of Rehabilitation and Communication Sciences, Ohio University, Athens
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14
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Vincze V, Szatlóczki G, Tóth L, Gosztolya G, Pákáski M, Hoffmann I, Kálmán J. Telltale silence: temporal speech parameters discriminate between prodromal dementia and mild Alzheimer's disease. CLINICAL LINGUISTICS & PHONETICS 2021; 35:727-742. [PMID: 32993390 DOI: 10.1080/02699206.2020.1827043] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 06/11/2023]
Abstract
This study presents a novel approach for the early detection of mild cognitive impairment (MCI) and mild Alzheimer's disease (mAD) in the elderly. Participants were 25 elderly controls (C), 25 clinically diagnosed MCI and 25 mAD patients, included after a clinical diagnosis validated by CT or MRI and cognitive tests. Our linguistic protocol involved three connected speech tasks that stimulate different memory systems, which were recorded, then analyzed linguistically by using the PRAAT software. The temporal speech-related parameters successfully differentiate MCI from mAD and C, such as speech rate, number and length of pauses, the rate of pause and signal. Parameters pauses/duration and silent pauses/duration linearly decreased among the groups, in other words, the percentage of pauses in the total duration of speech continuously grows as dementia progresses. Thus, the proposed approach may be an effective tool for screening MCI and mAD.
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Affiliation(s)
- Veronika Vincze
- MTA-SZTE Research Group on Artifical Intelligence, Szeged, Hungary
| | | | - László Tóth
- Institute of Informatics, University of Szeged, Szeged, Hungary
| | - Gábor Gosztolya
- MTA-SZTE Research Group on Artifical Intelligence, Szeged, Hungary
| | | | - Ildikó Hoffmann
- Department of Linguistics, University of Szeged, Szeged, Hungary
- Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary
| | - János Kálmán
- Department of Psychiatry, University of Szeged, Szeged, Hungary
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15
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16
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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. [PMID: 33833713 PMCID: PMC8021952 DOI: 10.3389/fpsyg.2021.620251] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 02/15/2021] [Indexed: 11/25/2022] Open
Abstract
Background: The field of voice and speech analysis has become increasingly popular over the last 10 years, and articles on its use in detecting neurodegenerative diseases have proliferated. Many studies have identified characteristic speech features that can be used to draw an accurate distinction between healthy aging among older people and those with mild cognitive impairment and Alzheimer's disease. Speech analysis has been singled out as a cost-effective and reliable method for detecting the presence of both conditions. In this research, a systematic review was conducted to determine these features and their diagnostic accuracy. Methods: Peer-reviewed literature was located across multiple databases, involving studies that apply new procedures of automatic speech analysis to collect behavioral evidence of linguistic impairments along with their diagnostic accuracy on Alzheimer's disease and mild cognitive impairment. The risk of bias was assessed by using JBI and QUADAS-2 checklists. Results: Thirty-five papers met the inclusion criteria; of these, 11 were descriptive studies that either identified voice features or explored their cognitive correlates, and the rest were diagnostic studies. Overall, the studies were of good quality and presented solid evidence of the usefulness of this technique. The distinctive acoustic and rhythmic features found are gathered. Most studies record a diagnostic accuracy over 88% for Alzheimer's and 80% for mild cognitive impairment. Conclusion: Automatic speech analysis is a promising tool for diagnosing mild cognitive impairment and Alzheimer's disease. The reported features seem to be indicators of the cognitive changes in older people. The specific features and the cognitive changes involved could be the subject of further research.
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Affiliation(s)
- Israel Martínez-Nicolás
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| | - Thide E Llorente
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
| | | | - Juan José G Meilán
- Faculty of Psychology, University of Salamanca, Salamanca, Spain.,Institute of Neuroscience of Castilla y León, University of Salamanca, Salamanca, Spain
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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; 27:1784-1797. [PMID: 32929494 PMCID: PMC7671617 DOI: 10.1093/jamia/ocaa174] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/14/2020] [Accepted: 07/14/2020] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVE In recent years numerous studies have achieved promising results in Alzheimer's Disease (AD) detection using automatic language processing. We systematically review these articles to understand the effectiveness of this approach, identify any issues and report the main findings that can guide further research. MATERIALS AND METHODS We searched PubMed, Ovid, and Web of Science for articles published in English between 2013 and 2019. We performed a systematic literature review to answer 5 key questions: (1) What were the characteristics of participant groups? (2) What language data were collected? (3) What features of speech and language were the most informative? (4) What methods were used to classify between groups? (5) What classification performance was achieved? RESULTS AND DISCUSSION We identified 33 eligible studies and 5 main findings: participants' demographic variables (especially age ) were often unbalanced between AD and control group; spontaneous speech data were collected most often; informative language features were related to word retrieval and semantic, syntactic, and acoustic impairment; neural nets, support vector machines, and decision trees performed well in AD detection, and support vector machines and decision trees performed well in decline detection; and average classification accuracy was 89% in AD and 82% in mild cognitive impairment detection versus healthy control groups. CONCLUSION The systematic literature review supported the argument that language and speech could successfully be used to detect dementia automatically. Future studies should aim for larger and more balanced datasets, combine data collection methods and the type of information analyzed, focus on the early stages of the disease, and report performance using standardized metrics.
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Affiliation(s)
- Ulla Petti
- Department of Theoretical and Applied Linguistics, University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Simon Baker
- Department of Theoretical and Applied Linguistics, University of Cambridge, Language Technology Lab, Cambridge, UK
| | - Anna Korhonen
- Department of Theoretical and Applied Linguistics, University of Cambridge, Language Technology Lab, Cambridge, UK
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18
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Momeni M, Rahmani M. Speech signal analysis of alzheimer's diseases in farsi using auditory model system. Cogn Neurodyn 2020; 15:453-461. [PMID: 34040671 DOI: 10.1007/s11571-020-09644-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 09/21/2020] [Accepted: 10/06/2020] [Indexed: 11/25/2022] Open
Abstract
In recent years, extensive studies have been conducted on the diagnosis of Alzheimer's disease (AD) using the non-invasive speech signal recognition method. In this study, Farsi speech signals were analyzed using the auditory model system (AMS) in order to recognize AD. For this purpose, after the pre-processing of the speech signals and utilizing AMS, 4D outputs as function of time, frequency, rate, and scale range were obtained. The AMS outcomes were averaged in term of time to analyze the rate-frequency-scale for both groups, Alzheimer's and healthy control subjects. Thereafter, the maximum of spectral and temporal modulation and frequency were extracted to classify by the support vector machine (SVM). The SVM achieves higher promising recognition accuracy with compare to prevalent approaches in the field of speech processing. The acceptable results demonstrate the applicability of the proposed algorithm in non-invasive and low-cost recognizing Alzheimer's only using the few extracted features of the speech signal.
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Affiliation(s)
- Maryam Momeni
- Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, Iran
| | - Mahdiyeh Rahmani
- Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, Iran
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Qiao Y, Xie XY, Lin GZ, Zou Y, Chen SD, Ren RJ, Wang G. Computer-Assisted Speech Analysis in Mild Cognitive Impairment and Alzheimer’s Disease: A Pilot Study from Shanghai, China. J Alzheimers Dis 2020; 75:211-221. [PMID: 32250297 DOI: 10.3233/jad-191056] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yuan Qiao
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xin-Yi Xie
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guo-Zhen Lin
- Department of Psychiatry, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Zou
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sheng-Di Chen
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ru-Jing Ren
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Gang Wang
- Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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20
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Ambrosini E, Cid M, de Isla CG, Salamanca P, Borghese NA, Ferrante S, Caielli M, Milis M, Loizou C, Azzolino D, Damanti S, Bertagnoli L, Cesari M, Moccia S. Automatic speech analysis to early detect functional cognitive decline in elderly population. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:212-216. [PMID: 31945880 DOI: 10.1109/embc.2019.8856768] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This study aimed at evaluating whether people with a normal cognitive function can be discriminated from subjects with a mild impairment of cognitive function based on a set of acoustic features derived from spontaneous speech. Voice recordings from 90 Italian subjects (age >65 years; group 1: 47 subjects with MMSE>26; group 2: 43 subjects with 20≤ MMSE ≤26) were collected. Voice samples were processed using a MATLAB-based custom software to derive a broad set of known acoustic features. Linear mixed model analyses were performed to select the features able to significantly distinguish between groups. The selected features (% of unvoiced segments, duration of unvoiced segments, % of voice breaks, speech rate, and duration of syllables), alone or in addition to age and years of education, were used to build a learning-based classifier. The leave-one-out cross validation was used for testing and the classifier accuracy was computed. When the voice features were used alone, an overall classification accuracy of 0.73 was achieved. When age and years of education were additionally used, the overall accuracy increased up to 0.80. These performances were lower than the accuracy of 0.86 found in a recent study. However, in that study the classification was based on several tasks, including more cognitive demanding tasks. Our results are encouraging because acoustic features, derived for the first time only from an ecologic continuous speech task, were able to discriminate people with a normal cognitive function from people with a mild cognitive decline. This study poses the basis for the development of a mobile application performing automatic voice analysis on-the-fly during phone calls, which might potentially support the detection of early signs of functional cognitive decline.
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Slegers A, Filiou RP, Montembeault M, Brambati SM. Connected Speech Features from Picture Description in Alzheimer's Disease: A Systematic Review. J Alzheimers Dis 2019; 65:519-542. [PMID: 30103314 DOI: 10.3233/jad-170881] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The language changes that occur over the course of Alzheimer's disease (AD) can impact communication abilities and have profound functional consequences. Picture description tasks can be used to approximate everyday communication abilities of AD patients. As various methods and variables have been studied over the years, current knowledge about the most affected features of AD discourse in the context of picture descriptions is difficult to summarize. This systematic review aims to provide researchers with an overview of the most common areas of impairment in AD discourse as they appear in picture description tasks. Based on the 44 articles fulfilling inclusion criteria, our findings reflect a multidimensional pattern of changes in the production (speech rate), syntactic (length of utterance), lexical (word-frequency and use of pronouns), fluency (repetitions and word-finding difficulties), semantic (information units), and discourse (efficiency) domains. We discuss our findings in the light of current research and point to potential scientific and clinical uses of picture description tasks in the context of AD.
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Affiliation(s)
- Antoine Slegers
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
| | - Renée-Pier Filiou
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
| | - Maxime Montembeault
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
| | - Simona Maria Brambati
- Département de Psychologie, Université de Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Montréal, QC, Canada
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22
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Martínez-Sánchez F, Meilán JJG, Carro J, Ivanova O. A Prototype for the Voice Analysis Diagnosis of Alzheimer's Disease. J Alzheimers Dis 2019; 64:473-481. [PMID: 29914025 DOI: 10.3233/jad-180037] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Speech variations enable us to map the performance of cognitive processes of syntactic, semantic, phonological, and articulatory planning and execution. Speaking is one of the first functions to be affected by neurodegenerative complaints such as Alzheimer's disease (AD), which makes the speech a highly promising biomarker for detecting the illness before the first preclinical symptoms appear. OBJECTIVE This paper has sought to develop and validate a technological prototype that adopts an automated approach to speech analysis among older people. METHODS It uses a mathematical algorithm based on certain discriminatory variables to estimate the probability of developing AD. RESULTS AND CONCLUSION This device may be used at a preclinical stage by non-expert health professionals to determine the likelihood of the onset of AD.
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Affiliation(s)
| | | | - Juan Carro
- Psychology Faculty, University of Salamanca, Salamanca, Spain
| | - Olga Ivanova
- Philology Faculty, University of Extremadura, Cáceres, Spain
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de la Fuente Garcia S, Ritchie CW, Luz S. Protocol for a conversation-based analysis study: PREVENT-ED investigates dialogue features that may help predict dementia onset in later life. BMJ Open 2019; 9:e026254. [PMID: 30918035 PMCID: PMC6475209 DOI: 10.1136/bmjopen-2018-026254] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 01/10/2019] [Accepted: 02/22/2019] [Indexed: 01/18/2023] Open
Abstract
INTRODUCTION Decreasing the incidence of Alzheimer's disease (AD) is a global public health priority. Early detection of AD is an important requisite for the implementation of prevention strategies towards this goal. While it is plausible that patients at the early stages of AD may exhibit subtle behavioural signs of neurodegeneration, neuropsychological testing seems unable to detect these signs in preclinical AD. Recent studies indicate that spontaneous speech data, which can be collected frequently and naturally, provide good predictors for AD detection in cohorts with a clinical diagnosis. The potential of models based on such data for detecting preclinical AD remains unknown. METHODS AND ANALYSIS The PREVENT-Elicitation of Dialogues (PREVENT-ED) study builds on the PREVENT Dementia project to investigate whether early behavioural signs of AD may be detected through dialogue interaction. Participants recruited through PREVENT, aged 40-59 at baseline, will be included in this study. We will use speech processing and machine learning methods to assess how well speech and visuospatial markers agree with neuropsychological, biomarker, clinical, lifestyle and genetic data from the PREVENT cohort. ETHICS AND DISSEMINATION There are no expected risks or burdens to participants. The procedures are not invasive and do not raise significant ethical issues. We only approach healthy consenting adults and all participants will be informed that this is an exploratory study and therefore has no diagnostic aim. Confidentiality aspects such as data encryption and storage comply with the General Data Protection Regulation and with the requirements from sponsoring bodies and ethical committees. This study has been granted ethical approval by the London-Surrey Research Ethics Committee (REC reference No: 18/LO/0860), and by Caldicott and Information Governance (reference No: CRD18048). PREVENT-ED results will be published in peer-reviewed journals.
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Affiliation(s)
- Sofia de la Fuente Garcia
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh School of Molecular Genetic and Population Health Sciences, Edinburgh, UK
| | - Craig W Ritchie
- Centre for Clinical Brain Sciences, Department of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Saturnino Luz
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh School of Molecular Genetic and Population Health Sciences, Edinburgh, UK
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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. [DOI: 10.1016/j.csl.2018.07.007] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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25
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Tóth L, Hoffmann I, Gosztolya G, Vincze V, Szatlóczki G, Bánréti Z, Pákáski M, Kálmán J. A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech. Curr Alzheimer Res 2018; 15:130-138. [PMID: 29165085 PMCID: PMC5815089 DOI: 10.2174/1567205014666171121114930] [Citation(s) in RCA: 98] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 09/10/2017] [Accepted: 11/15/2017] [Indexed: 11/22/2022]
Abstract
BACKGROUND Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. METHODS Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. RESULTS The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process - that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. CONCLUSION The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.
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Affiliation(s)
- László Tóth
- MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
| | - Ildikó Hoffmann
- Linguistics Department, University of Szeged, Szeged, Hungary
- Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary
| | - Gábor Gosztolya
- MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
| | - Veronika Vincze
- MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
| | | | - Zoltán Bánréti
- Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary
| | | | - János Kálmán
- Department of Psychiatry, University of Szeged, Szeged, Hungary
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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. [PMID: 26539107 PMCID: PMC4611852 DOI: 10.3389/fnagi.2015.00195] [Citation(s) in RCA: 94] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 09/28/2015] [Indexed: 12/02/2022] Open
Abstract
It is known that Alzheimer’s disease (AD) influences the temporal characteristics of spontaneous speech. These phonetical changes are present even in mild AD. Based on this, the question arises whether an examination based on language analysis could help the early diagnosis of AD and if so, which language and speech characteristics can identify AD in its early stage. The purpose of this article is to summarize the relation between prodromal and manifest AD and language functions and language domains. Based on our research, we are inclined to claim that AD can be more sensitively detected with the help of a linguistic analysis than with other cognitive examinations. The temporal characteristics of spontaneous speech, such as speech tempo, number of pauses in speech, and their length are sensitive detectors of the early stage of the disease, which enables an early simple linguistic screening for AD. However, knowledge about the unique features of the language problems associated with different dementia variants still has to be improved and refined.
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Affiliation(s)
- Greta Szatloczki
- Research Institute for Linguistics, Hungarian Academy of Sciences , Szeged , Hungary
| | - Ildiko Hoffmann
- Research Institute for Linguistics, Hungarian Academy of Sciences , Budapest , Hungary ; Department of Linguistics, University of Szeged , Szeged , Hungary
| | - Veronika Vincze
- MTA-SZTE Research Group on Artificial Intelligence, University of Szeged , Szeged , Hungary
| | - Janos Kalman
- Research Institute for Linguistics, Hungarian Academy of Sciences , Szeged , Hungary
| | - Magdolna Pakaski
- Research Institute for Linguistics, Hungarian Academy of Sciences , Szeged , Hungary
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König A, Satt A, Sorin A, Hoory R, Toledo-Ronen O, Derreumaux A, Manera V, Verhey F, Aalten P, Robert PH, David R. Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2015; 1:112-24. [PMID: 27239498 PMCID: PMC4876915 DOI: 10.1016/j.dadm.2014.11.012] [Citation(s) in RCA: 125] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Background To evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early-stage Alzheimer's disease (AD). Methods Healthy elderly control (HC) subjects and patients with MCI or AD were recorded while performing several short cognitive vocal tasks. The voice recordings were processed, and the first vocal markers were extracted using speech signal processing techniques. Second, the vocal markers were tested to assess their “power” to distinguish among HC, MCI, and AD. The second step included training automatic classifiers for detecting MCI and AD, using machine learning methods and testing the detection accuracy. Results The classification accuracy of automatic audio analyses were as follows: between HCs and those with MCI, 79% ± 5%; between HCs and those with AD, 87% ± 3%; and between those with MCI and those with AD, 80% ± 5%, demonstrating its assessment utility. Conclusion Automatic speech analyses could be an additional objective assessment tool for elderly with cognitive decline.
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Affiliation(s)
- Alexandra König
- Research Unit CoBTeK - Cognition Behaviour Technology, Edmond & Lily Safra Research Center, University of Nice Sophia Antipolis, Nice, France; Alzheimer Centre Limburg, Maastricht University Medical Center, School for Mental Health and Neuroscience, Maastricht, The Netherlands
| | - Aharon Satt
- Speech Technologies, IBM Research, Haifa, Israel
| | | | - Ron Hoory
- Speech Technologies, IBM Research, Haifa, Israel
| | | | - Alexandre Derreumaux
- Research Unit CoBTeK - Cognition Behaviour Technology, Edmond & Lily Safra Research Center, University of Nice Sophia Antipolis, Nice, France
| | - Valeria Manera
- Research Unit CoBTeK - Cognition Behaviour Technology, Edmond & Lily Safra Research Center, University of Nice Sophia Antipolis, Nice, France
| | - Frans Verhey
- Alzheimer Centre Limburg, Maastricht University Medical Center, School for Mental Health and Neuroscience, Maastricht, The Netherlands
| | - Pauline Aalten
- Alzheimer Centre Limburg, Maastricht University Medical Center, School for Mental Health and Neuroscience, Maastricht, The Netherlands
| | - Phillipe H Robert
- Research Unit CoBTeK - Cognition Behaviour Technology, Edmond & Lily Safra Research Center, University of Nice Sophia Antipolis, Nice, France; Centre Mémoire de Ressources et de Recherche, CHU de Nice, Nice, France
| | - Renaud David
- Research Unit CoBTeK - Cognition Behaviour Technology, Edmond & Lily Safra Research Center, University of Nice Sophia Antipolis, Nice, France; Centre Mémoire de Ressources et de Recherche, CHU de Nice, Nice, France
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28
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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 2015; 37:327-34. [PMID: 24481220 DOI: 10.1159/000356726] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/15/2013] [Indexed: 11/19/2022] Open
Abstract
AIMS The study explores how speech measures may be linked to language profiles in participants with Alzheimer's disease (AD) and how these profiles could distinguish AD from changes associated with normal aging. METHODS We analysed simple sentences spoken by older adults with and without AD. Spectrographic analysis of temporal and acoustic characteristics was carried out using the Praat software. RESULTS We found that measures of speech, such as variations in the percentage of voice breaks, number of periods of voice, number of voice breaks, shimmer (amplitude perturbation quotient), and noise-to-harmonics ratio, characterise people with AD with an accuracy of 84.8%. DISCUSSION These measures offer a sensitive method of assessing spontaneous speech output in AD, and they discriminate well between people with AD and healthy older adults. This method of evaluation is a promising tool for AD diagnosis and prognosis, and it could be used as a dependent measure in clinical trials.
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Affiliation(s)
- Juan José G Meilán
- Institute of Neurosciences of Castilla y León, University of Salamanca, Salamanca, Spain
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López-de-Ipiña K, Alonso JB, Travieso CM, Solé-Casals J, Egiraun H, Faundez-Zanuy M, Ezeiza A, Barroso N, Ecay-Torres M, Martinez-Lage P, de Lizardui UM. On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis. SENSORS (BASEL, SWITZERLAND) 2013; 13:6730-45. [PMID: 23698268 PMCID: PMC3690078 DOI: 10.3390/s130506730] [Citation(s) in RCA: 82] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Revised: 05/08/2013] [Accepted: 05/13/2013] [Indexed: 11/16/2022]
Abstract
The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.
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Affiliation(s)
- Karmele López-de-Ipiña
- Systems Engineering and Automation Department, University of the Basque Country UPV/EHU, Donostia 20018, Spain; E-Mails: (H.E.); (A.E.); (N.B.); (U.M.L.)
| | - Jesus-Bernardino Alonso
- Signal and Communication Departament (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), Campus of Tafira, Las Palmas de Gran Canaria 35017, Spain; E-Mails: (J.-B.A.); (C.M.T.)
| | - Carlos Manuel Travieso
- Signal and Communication Departament (DSC), Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria (ULPGC), Campus of Tafira, Las Palmas de Gran Canaria 35017, Spain; E-Mails: (J.-B.A.); (C.M.T.)
| | - Jordi Solé-Casals
- Digital Technologies Group, University of Vic, Sagrada família 7, Vic 08500, Spain; E-Mail:
| | - Harkaitz Egiraun
- Systems Engineering and Automation Department, University of the Basque Country UPV/EHU, Donostia 20018, Spain; E-Mails: (H.E.); (A.E.); (N.B.); (U.M.L.)
- Research Center for Experimental Marine Biology and Biotechnology, Plentzia Marine Station, University of the Basque Country, Plentzia 48620, Spain
| | - Marcos Faundez-Zanuy
- Escola Universitaria Politècnica de Mataró (UPC), Tecnocampus, Mataró, Barcelona 08302, Spain; E-Mail:
| | - Aitzol Ezeiza
- Systems Engineering and Automation Department, University of the Basque Country UPV/EHU, Donostia 20018, Spain; E-Mails: (H.E.); (A.E.); (N.B.); (U.M.L.)
| | - Nora Barroso
- Systems Engineering and Automation Department, University of the Basque Country UPV/EHU, Donostia 20018, Spain; E-Mails: (H.E.); (A.E.); (N.B.); (U.M.L.)
| | - Miriam Ecay-Torres
- CITA-Alzheimer Foundation, San Sebastian 20009, Spain; E-Mails: (M.E.-T.); (P.M.-L.)
| | - Pablo Martinez-Lage
- CITA-Alzheimer Foundation, San Sebastian 20009, Spain; E-Mails: (M.E.-T.); (P.M.-L.)
| | - Unai Martinez de Lizardui
- Systems Engineering and Automation Department, University of the Basque Country UPV/EHU, Donostia 20018, Spain; E-Mails: (H.E.); (A.E.); (N.B.); (U.M.L.)
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