Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA)

Kantithammakorn, Pimarn; Punyabukkana, Proadpran; Pratanwanich, Naruemon; Hemrungrojn, Solaphat; Chunharas, Chaipat; Wanvarie, Dittaya

doi:10.3390/s22041583

Cited by 8 publications

(7 citation statements)

References 29 publications

(25 reference statements)

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“…We believe that selecting a significant feature from the various tasks will encourage the performance of MCI detection or other relevant diseases (dementia and AD). We also plan to use the Thai text-to-speech solution [ 10 ] that focuses on PVF in terms of being fully automated.…”

Section: Discussionmentioning

confidence: 99%

“…Participants were assessed via MoCA application for their cognition ( Figure 2 ) [ 10 ]. Voice data were recorded in .m4a file format at 44.1 kHz, 32 bits, via an iPad’s microphone.…”

Section: Methodsmentioning

confidence: 99%

“…The Montreal cognitive assessment (MoCA) is a prominent screening assessment tool to diagnose cognitive impairment [ 6 , 7 , 8 , 9 , 10 ]. MoCA is used to diagnose MCI by considering patients’ performance in various cognitive functions using tests.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we focused on detecting MCI using Thai PVF data from the digital MoCA [ 10 ], which has validity as assessed by examining Spearman’s rank order coefficients and the Cronbach alpha value [ 20 ]. To solve the language barriers, we planned to use our proficiency in Thai language to develop a novel phonemic clustering and switching algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments

Metarugcheep

Punyabukkana

Wanvarie

et al. 2022

Sensors

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Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as “F” in English and “ก” /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI. The PVF features, including acoustic features, semantic features, and word grouping, have been studied in many languages but not Thai. However, applying the same PVF feature extraction methods used in English to Thai yields unpleasant results due to different language characteristics. This study performs analytical feature extraction on Thai PVF data to classify MCI patients. In particular, we propose novel approaches to extract features based on phonemic clustering (ability to cluster words by phonemes) and switching (ability to shift between clusters) for the Thai PVF data. The comparison results of the three classifiers revealed that the support vector machine performed the best with an area under the receiver operating characteristic curve (AUC) of 0.733 (N = 100). Furthermore, our implemented guidelines extracted efficient features, which support the machine learning models regarding MCI detection on Thai PVF data.

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Section: Discussionmentioning

confidence: 99%

“…Participants were assessed via MoCA application for their cognition ( Figure 2 ) [ 10 ]. Voice data were recorded in .m4a file format at 44.1 kHz, 32 bits, via an iPad’s microphone.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments

Metarugcheep

Punyabukkana

Wanvarie

et al. 2022

Sensors

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“…Computer-based cognitive tests provided by the NIH-Toolbox are also commonly used as standardized measures of cognition for insights into different cognitive domains 16 . While these cognitive measures can be readily measured, they depend on paper or digital text input formats limited by the patient's motor and language pro ciency, as well as by trained examiners during data collection and scoring 17 .…”

Section: Introductionmentioning

confidence: 99%

Resting-state EEG measures cognitive impairment in Parkinson’s disease

Anjum

Espinoza

Cole

et al. 2023

Preprint

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Background: Cognitive dysfunction is common in Parkinson’s disease (PD) and is diagnosed by complex, time-consuming psychometric tests which are affected by language and education, subject to learning effects, and not suitable for continuous monitoring of cognition. Objectives: We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. Methods: We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from the National Institutes of Health (NIH) Toolbox using cross-validation schemes, regression models, and randomization tests. Results: We observed cognition-related changes in EEG activities over multiple spectral rhythms. Utilizing only 8 best-performing EEG electrodes, our proposed index strongly correlated with cognition (rho = 0.68, p value < 0.001 with MoCA; rho ≥ 0.56, p value < 0.001 with cognitive tests from the NIH Toolbox) outperforming traditional spectral markers (rho = -0.30 – 0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Conclusions: Our approach is computationally efficient for real-time indexing of cognition across domains, implementable even in hardware with limited computing capabilities, making it potentially compatible with dynamic therapies such as closed-loop neurostimulation, and will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

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