The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A New iPhone Application for Voice Quality Assessment Based on the GRBAS Scale
Open accessAbstract
View Video S1 Laryngoscope, 131:580–582, 2021
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“While optimal conditions favor reproducibility, they only partially reflect real clinical contexts where ambient noise, reverberation and recording device variability are frequent [46,47].”
From Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review (2026) · cites this paper as [46]
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In the citing paper
“Robustness of automated detection systems to ambient noise represents a critical issue for clinical applicability, yet few studies explicitly examine noise impact under realistic conditions [46,50].”
From Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review (2026) · cites this paper as [46]
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Citations
Cites
Listener Experience and Perception of Voice Quality
Kreiman, J. E., Gerratt, B. R., & Precoda, K. (1990) · Journal of Speech Language and Hearing Research · doi:10.1044/jshr.3301.103 · Cited by 222
Increasing and Evolving Role of Smart Devices in Modern Medicine
Massoomi, M. R., & Handberg, E. M. (2019) · European Cardiology Review · doi:10.15420/ecr.2019.02 · Cited by 64
Classification of Voice Disorders Using a One-Dimensional Convolutional Neural Network
Fujimura, S., Kojima, T., Okanoue, Y., Shoji, K., Inoue, M., Omori, K., & Hori, R. (2022) · Journal of Voice · doi:10.1016/j.jvoice.2020.02.009 · Cited by 58
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Hirano M, 1981, Disorders of Human Communication, 5, Clinical Examination of Voice, 81
Cited by
Sorted by citations
Objective Assessment of Pathological Voice Using Artificial Intelligence Based on the GRBAS Scale
Kojima, T., Fujimura, S., Hasebe, K., Okanoue, Y., Shuya, O., Yuki, R., Shoji, K., Hori, R., Kishimoto, Y., & Omori, K. (2024) · Journal of Voice · doi:10.1016/j.jvoice.2021.11.021 · Cited by 27
Self-citationDiagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review
Mairesse, S., Maniaci, A., Briganti, G., & Lechien, J. R. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16060301 · Cited by 1
“While optimal conditions favor reproducibility, they only partially reflect real clinical contexts where ambient noise, reverberation and recording device variability are frequent [46,47].”
Metadata
- Authors
- Kojima, T., Hasebe, K., Fujimura, S., Okanoue, Y., Kagoshima, H., Taguchi, A., Yamamoto, H., Shoji, K., & Hori, R.
- Affiliations
- Tenri Hospital (JP)
- Kyoto University (JP)
- Corresponding author
- Kojima, Tsuyoshi
- ORCID
- Venue
- The Laryngoscope
- Publisher
- Wiley
- Published
- 2020-07-09
- Volume
- 131
- Issue
- 3
- Pages
- 580-582
- ISSN
- 0023-852X (ISSN-L)
- 1531-4995
- OpenAlex
- W3041859432
- Cited by
- 13
- References
- 3
- Citations by year
- 2026: 2 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 1
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Laryngoscope (publishedVersion)
- PubMed (publishedVersion)
- Funders
- JSPS KAKENHI (JP18K18407)
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- voice quality assessment, GRBAS scale, iOS app, voice disorders, mobile health apps
- MeSH terms
- Deep Learning, Hoarseness, Humans, Speech Production Measurement, Voice Disorders, Voice Quality, Reproducibility of Results, Cell Phone, Mobile Applications
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