The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Emotional speech recognition: Resources, features, and methods
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Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646
Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465
Speech Analysis Synthesis and Perception
Flanagan, J. L. (1972) · doi:10.1007/978-3-662-01562-9 · Cited by 1,469
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
Nonlinear feature based classification of speech under stress.
Zhou, G., Hansen, J. H. L., & Kaiser, J. F. (2001) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.905995 · Cited by 409
The biological affects: a typology.
Buck, R. (1999) · Psychological Review · doi:10.1037/0033-295x.106.2.301 · Cited by 346
Effect of experimentally induced stress on vocal parameters.
Tolkmitt, F. J., & Scherer, K. R. (1986) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.12.3.302 · Cited by 184
Acoustic correlates of task load and stress
Scherer, K. R., Grandjean, D., Johnstone, T., Klasmeyer, G., & Bänziger, T. (2002) · 7th International Conference on Spoken Language Processing (ICSLP 2002) · doi:10.21437/icslp.2002-554 · Cited by 84
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
Akçay, M. B., & Oğuz, K. (2020) · Speech Communication · doi:10.1016/j.specom.2019.12.001 · Cited by 721
Speech Emotion Recognition Using Deep Learning Techniques: A Review
Khalil, R. A., Jones, E., Babar, M. I., Jan, T., Zafar, M. H., & Alhussain, T. (2019) · IEEE Access · doi:10.1109/access.2019.2936124 · Cited by 702
Mao, Q., Dong, M., Huang, Z., & Zhan, Y. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2360798 · Cited by 568
Evaluating deep learning architectures for Speech Emotion Recognition
Fayek, H. M., Lech, M., & Cavedon, L. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.02.013 · Cited by 554
Metadata
- Authors
- Ververidis, D., & Kotropoulos, C.
- Venue
- Speech Communication
- Publisher
- Elsevier BV
- Published
- 2006-09
- Volume
- 48
- Issue
- 9
- Pages
- 1162-1181
- ISSN
- 0167-6393 (ISSN-L)
- OpenAlex
- W2137639365
- Cited by
- 925
- References
- 10
- FWCI
- 33.86
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
