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Cited by 290Dementia and Cognitive Impairment ResearchSpeech Recognition and SynthesisVoice and Speech Disorders

The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.

A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech

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Toth, Laszlo, Hoffmann, Ildiko, Gosztolya, Gabor, Vincze, Veronika, Szatloczki, Greta, Banreti, Zoltan, Pakaski, Magdolna, Kalman, Janos·2018·Current Alzheimer Research
DOI 10.2174/1567205014666171121114930PMC PMC5815089
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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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Fig. (1) (a). The ROC curve for the Naive Bayes classifier. (b). The ROC curve for the SVM classifier. (c). The ROC curve for the Random Forest classifier.
Fig. (1) (a). The ROC curve for the Naive Bayes classifier. (b). The ROC curve for the SVM classifier. (c). The ROC curve for the Random Forest classifier.CC BY-NC · PubMed Central

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Research works printed but not linked: 3

  1. [36]Hall M. Frank E. Holmes G. Pfahringer B. Reutemann P. Witten I. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 2009 11 1 10 18
  2. [37]Freedman M.I. Leach L. Kaplan E. Winocur G. Shulman K.J. Delis D.C. Clock drawing. Oxford Oxford University Press 1994
  3. [44]Szabó L. Tarján B. Mihajlik P. Fegyó T. Speeding up error correction in a Hungarian dictation system (in Hungarian Proceedings of MSZNY 182 91 2015

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Metadata

Authors
Toth, L., Hoffmann, I., Gosztolya, G., Vincze, V., Szatloczki, G., Banreti, Z., Pakaski, M., & Kalman, J.
Affiliations
  • MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary
  • Linguistics Department, University of Szeged, Szeged, Hungary
  • MTA-SZTE Research Group on Artificial Intelligence (HU)
  • Department of Psychiatry, University of Szeged, Szeged, Hungary
  • Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary
ORCID
Vincze, Veronika
Venue
Current Alzheimer Research
Publisher
Bentham Science Publishers Ltd.
Published
2018-01-03
Volume
15
Issue
2
Pages
130-138
ISSN
1567-2050 (ISSN-L)
DOI
10.2174/1567205014666171121114930
PMC
PMC5815089
OpenAlex
W2770604845
Cited by
290
References
49
Citations by year
2026: 39 · 2025: 37 · 2024: 42 · 2023: 46 · 2022: 31 · 2021: 44 · 2020: 26 · 2019: 19 · 2018: 3
FWCI
11.22
Type
journal article
Language
en
Open access
Locations
  • Current Alzheimer Research (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • SZTE Publicatio Repozitórium (University of Szeged) (acceptedVersion, other-oa)
  • Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
Funders
Magyar Tudományos Akadémia
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
Keywords
mild cognitive impairment, Alzheimer's disease, spontaneous speech, speech tempo, acoustic features, speech hesitations, pauses, neuropsychological screening, automated detection, memory retrieval
MeSH terms
Machine Learning, Aged, Aged, 80 and over, Diagnosis, Computer-Assisted, Female, Humans, Male, Memory, Middle Aged, Neuropsychological Tests, Pattern Recognition, Automated, ROC Curve, Speech, Speech Production Measurement, Models, Statistical, Internet, Speech Recognition Software, Cognitive Dysfunction

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