Treffer: Cross-device and test-retest reliability of speech acoustic measurements derived from consumer-grade mobile recording devices.
Title:
Cross-device and test-retest reliability of speech acoustic measurements derived from consumer-grade mobile recording devices.
Authors:
Hu Z; Institutes of Physical Science and Information Technology, Anhui University, Hefei, China.; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China., Zhang Z; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.; University of Science and Technology of China, Hefei, China., Li H; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China. hli@cmpt.ac.cn.; University of Science and Technology of China, Hefei, China. hli@cmpt.ac.cn.; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China. hli@cmpt.ac.cn., Yang LZ; Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China. lzyang@cmpt.ac.cn.; University of Science and Technology of China, Hefei, China. lzyang@cmpt.ac.cn.; Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China. lzyang@cmpt.ac.cn.
Source:
Behavior research methods [Behav Res Methods] 2024 Dec 30; Vol. 57 (1), pp. 35. Date of Electronic Publication: 2024 Dec 30.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Springer Country of Publication: United States NLM ID: 101244316 Publication Model: Electronic Cited Medium: Internet ISSN: 1554-3528 (Electronic) Linking ISSN: 1554351X NLM ISO Abbreviation: Behav Res Methods Subsets: MEDLINE
Imprint Name(s):
Publication: 2010- : New York : Springer
Original Publication: Austin, Tex. : Psychonomic Society, c2005-
Original Publication: Austin, Tex. : Psychonomic Society, c2005-
MeSH Terms:
References:
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Almaghrabi, S. A., Thewlis, D., Thwaites, S., Rogasch, N. C., Lau, S., Clark, S. R., & Baumert, M. (2022). The reproducibility of bio-acoustic features is associated with sample duration, speech task, and gender. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 167–175. https://doi.org/10.1109/TNSRE.2022.3143117. (PMID: 10.1109/TNSRE.2022.314311735038295)
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Bottalico, P., Codino, J., Cantor-Cutiva, L. C., Marks, K., Nudelman, C. J., Skeffington, J., Shrivastav, R., Jackson-Menaldi, M. C., Hunter, E. J., & Rubin, A. D. (2020). Reproducibility of voice parameters: The effect of room acoustics and microphones. Journal of Voice, 34(3), 320–334. https://doi.org/10.1016/j.jvoice.2018.10.016. (PMID: 10.1016/j.jvoice.2018.10.01630471944)
Busquet, F., Efthymiou, F., & Hildebrand, C. (2023). Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices. Behavior Research Methods, 56(3), 2114–2134. https://doi.org/10.3758/s13428-023-02139-9. (PMID: 10.3758/s13428-023-02139-93725395810228884)
Cai, H., Huang, X., Liu, Z., Liao, W., Dai, H., Wu, Z., Zhu, D., Ren, H., Li, Q., Liu, T., & Li, X. (2023). Exploring multimodal approaches for Alzheimer’s disease detection using patient speech transcript and audio data. arXiv preprint. https://doi.org/10.48550/arXiv.2307.02514.
Cavalcanti, J. C., Englert, M., Oliveira, M., & Constantini, A. C. (2023). Microphone and audio compression effects on acoustic voice analysis: A pilot study. Journal of Voice, 37(2), 162–172. https://doi.org/10.1016/j.jvoice.2020.12.005. (PMID: 10.1016/j.jvoice.2020.12.00533451892)
Coulacoglou, C., & Saklofske, D. H. (2017). Psychometrics and psychological assessment: Principles and applications. Elsevier/AP, Academic Press. an imprint of Elsevier.
De La Fuente Garcia, S., Ritchie, C. W., & Luz, S. (2020). Artificial Intelligence, speech, and language processing approaches to monitoring Alzheimer’s disease: A systematic seview. Journal of Alzheimer’s Disease, 78(4), 1547–1574. https://doi.org/10.3233/JAD-200888. (PMID: 10.3233/JAD-200888331856057836050)
De Oliveira Florencio, V., Almeida, A. A., Balata, P., Nascimento, S., Brockmann-Bauser, M., & Lopes, L. W. (2021). Differences and reliability of linear and nonlinear acoustic measures as a function of vocal intensity in individuals with voice disorders. Journal of Voice, S0892199721001442. https://doi.org/10.1016/j.jvoice.2021.04.011.
Feng, F., Zhang, Z., Tang, L., Qian, H., Yang, L.-Z., Jiang, H., & Li, H. (2024). Test-retest reliability of acoustic and linguistic measures of speech tasks. Computer Speech & Language, 83, 101547. https://doi.org/10.1016/j.csl.2023.101547. (PMID: 10.1016/j.csl.2023.101547)
Fristed, E., Skirrow, C., Meszaros, M., Lenain, R., Meepegama, U., Papp, K. V., Ropacki, M. T., & Weston, J. (2022). A simple, automated, speech-based and AI-enhanced screener for mild cognitive impairment and amyloid beta positivity. Alzheimer’s & Dementia, 18, e065647. (PMID: 10.1002/alz.065647)
Fujimura, S., Kojima, T., Okanoue, Y., Kagoshima, H., Taguchi, A., Shoji, K., Inoue, M., & Hori, R. (2020). Real-time acoustic voice analysis using a handheld device running Android operating system. Journal of Voice, 34(6), 823–829. https://doi.org/10.1016/j.jvoice.2019.05.013. (PMID: 10.1016/j.jvoice.2019.05.01331253388)
García, A. M., Johann, F., Echegoyen, R., Calcaterra, C., Riera, P., Belloli, L., & Carrillo, F. (2023). Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02240-z.
Godino-Llorente, J. I., Shattuck-Hufnagel, S., Choi, J. Y., Moro-Velázquez, L., & Gómez-García, J. A. (2017). Towards the identification of Idiopathic Parkinson’s disease from the speech. New articulatory kinetic biomarkers. PLOS ONE, 12(12), e0189583. https://doi.org/10.1371/journal.pone.0189583. (PMID: 10.1371/journal.pone.018958329240814)
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Grant Information:
82371931 Natural Science Fund of China; YZJJ202207-TS HFIPS Director's Fund; 202204295107020004 Anhui Province Key Research and Development Project
Contributed Indexing:
Keywords: Acoustic analysis; Consumer-grade devices; Reliability; Remote speech assessment
Entry Date(s):
Date Created: 20241231 Date Completed: 20241231 Latest Revision: 20250307
Update Code:
20250307
DOI:
10.3758/s13428-024-02584-0
PMID:
39738817
Database:
MEDLINE
Weitere Informationen
In recent years, there has been growing interest in remote speech assessment through automated speech acoustic analysis. While the reliability of widely used features has been validated in professional recording settings, it remains unclear how the heterogeneity of consumer-grade recording devices, commonly used in nonclinical settings, impacts the reliability of these measurements. To address this issue, we systematically investigated the cross-device and test-retest reliability of classical speech acoustic measurements in a sample of healthy Chinese adults using consumer-grade equipment across three popular speech tasks: sustained phonation (SP), diadochokinesis (DDK), and picture description (PicD). A total of 51 participants completed two recording sessions spaced at least 24 hours apart. Speech outputs were recorded simultaneously using four devices: a voice recorder, laptop, tablet, and smartphone. Our results demonstrated good reliability for fundamental frequency and cepstral peak prominence in the SP task across testing sessions and devices. Other features from the SP and PicD tasks exhibited acceptable test-retest reliability, except for the period perturbation quotient from the tablet and formant frequency from the smartphone. However, measures from the DDK task showed a significant decrease in reliability on consumer-grade recording devices compared to professional devices. These findings indicate that the lower recording quality of consumer-grade equipment may compromise the reproducibility of syllable rate estimation, which is critical for DDK analysis. This study underscores the need for standardization of remote speech monitoring methodologies to ensure that remote home assessment provides accurate and reliable results for early screening.
(© 2024. The Psychonomic Society, Inc.)
Declarations. Conflicts of interest: The authors declare they have no financial interests. Ethics approval: The study was approved by the ethics committee of the Hefei Institutes of Physical Science and was conducted in accordance with the Declaration of Helsinki. Consent to participate: Informed consent was obtained from all individual participants included in the study. Consent for publication: The present study does not include any images, videos, or textual data from participants. The analyzed data consist solely of abstract acoustic features, which cannot convey any personal information.