The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech
Abstract
The motivation for this work is in an attempt to rectify the current lack of objective tools for clinical analysis of emotional disorders. This study involves the examination of a large breadth of objectively measurable features for use in discriminating depressed speech. Analysis is based on features related to prosodics, the vocal tract, and parameters extracted directly from the glottal waveform. Discrimination of the depressed speech was based on a feature selection strategy utilizing the following combinations of feature domains: prosodic measures alone, prosodic and vocal tract measures, prosodic and glottal measures, and all three domains. The combination of glottal and prosodic features produced better discrimination overall than the combination of prosodic and vocal tract features. Analysis of discriminating feature sets used in the study reflect a clear indication that glottal descriptors are vital components of vocal affect analysis.
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Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.6.617 · Cited by 12,052
Acoustical properties of speech as indicators of depression and suicidal risk
France, D. J., Shiavi, R. G., Silverman, S., Silverman, M., & Wilkes, M. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.846676 · Cited by 504
Cited by
The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., Andre, E., Busso, C., Devillers, L. Y., Epps, J., Laukka, P., Narayanan, S. S., et al. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2457417 · Cited by 1,818
Detection of Clinical Depression in Adolescents’ Speech During Family Interactions
Low, L.-S. A., Maddage, M. C., Lech, M., Sheeber, L. B., & Allen, N. B. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2091640 · Cited by 256
Deep learning for depression recognition with audiovisual cues: A review
He, L., Niu, M., Tiwari, P., Marttinen, P., Su, R., Jiang, J., Guo, C., Wang, H., Ding, S., Wang, Z., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.10.012 · Cited by 253
Passive and In-Situ assessment of mental and physical well-being using mobile sensors
Rabbi, M., Ali, S., Choudhury, T., & Berke, E. (2011) · Proceedings of the 13th international conference on Ubiquitous computing · doi:10.1145/2030112.2030164 · Cited by 243
Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., et al. (2020) · Journal of Counseling Psychology · doi:10.1037/cou0000382 · Cited by 141
Metadata
- Authors
- Moore, E., Clements, M. A., Peifer, J. W., & Weisser, L.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2008-01
- Volume
- 55
- Issue
- 1
- Pages
- 96-107
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2103011877
- Cited by
- 220
- References
- 2
- FWCI
- 3.75
- Type
- journal article
- Language
- en
- Open access
- closed
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- Moore, E., Clements, M. A., Peifer, J. W., & Weisser, L. (2008). Critical Analysis of the Impact of Glottal Features in the Classification of Clinical Depression in Speech. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2007.900562
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