The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Emotion recognition from speech: a review
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Citations
Cites
A database of German emotional speech
Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005) · Interspeech 2005 · doi:10.21437/interspeech.2005-446 · Cited by 2,365
Toward detecting emotions in spoken dialogs
Lee, C. M., & Narayanan, S. S. (2005) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.838534 · Cited by 988
Emotions and speech: some acoustical correlates.
Williams, C. E., & Stevens, K. N. (1972) · The Journal of the Acoustical Society of America · doi:10.1121/1.1913238 · Cited by 735 · cited 1×
“Generally, it is found that acted/simulated emotions tend to be more expressive than real ones (Ayadi et al. 2011; Williams and Stevens 1972).”
BackgroundAcoustical 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
Class-level spectral features for emotion recognition
Bitouk, D., Verma, R., & Nenkova, A. (2010) · Speech Communication · doi:10.1016/j.specom.2010.02.010 · Cited by 219
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
Automatic speech emotion recognition using recurrent neural networks with local attention
Mirsamadi, S., Barsoum, E., & Zhang, C. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952552 · Cited by 770
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
Attention Based Fully Convolutional Network for Speech Emotion Recognition
Zhang, Y., Du, J., Wang, Z., Zhang, J., & Tu, Y. (2018) · 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659587 · Cited by 161
Reichenberger, J., Schnepper, R., Arend, A.-K., & Blechert, J. (2020) · Proceedings of the Nutrition Society · doi:10.1017/s0029665120007004 · Cited by 161
Metadata
- Authors
- Koolagudi, S. G., & Rao, K. S.
- Venue
- International Journal of Speech Technology
- Published
- 2012-01-04
- Volume
- 15
- Issue
- 2
- Pages
- 99-117
- ISSN
- 1381-2416 (ISSN-L)
- 1572-8110
- OpenAlex
- W2061068689
- Cited by
- 641
- References
- 7
- FWCI
- 36.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Emotion recognition from speech: a review. https://app.cochat.ai/papers/10.1007/s10772-011-9125-1
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- Koolagudi, S. G., & Rao, K. S. (2012). Emotion recognition from speech: a review. International Journal of Speech Technology. https://doi.org/10.1007/s10772-011-9125-1
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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.
