The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset
Open accessAbstract
People convey their emotional state in their face and voice. We present an audio-visual data set uniquely suited for the study of multi-modal emotion expression and perception. The data set consists of facial and vocal emotional expressions in sentences spoken in a range of basic emotional states (happy, sad, anger, fear, disgust, and neutral). 7,442 clips of 91 actors with diverse ethnic backgrounds were rated by multiple raters in three modalities: audio, visual, and audio-visual. Categorical emotion labels and real-value intensity values for the perceived emotion were collected using crowd-sourcing from 2,443 raters. The human recognition of intended emotion for the audio-only, visual-only, and audio-visual data are 40.9%, 58.2% and 63.6% respectively. Recognition rates are highest for neutral, followed by happy, anger, disgust, fear, and sad. Average intensity levels of emotion are rated highest for visual-only perception. The accurate recognition of disgust and fear requires simultaneous audio-visual cues, while anger and happiness can be well recognized based on evidence from a single modality. The large dataset we introduce can be used to probe other questions concerning the audio-visual perception of emotion.
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Barsoum, E., Zhang, C., Ferrer, C. C., & Zhang, Z. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2993165 · Cited by 926
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception
Busso, C., Parthasarathy, S., Burmania, A., AbdelWahab, M., Sadoughi, N., & Provost, E. M. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2515617 · Cited by 391
ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis
He, Y., Gan, B., Chen, S., Zhou, Y., Yin, G., Song, L., Sheng, L., Shao, J., & Liu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00434 · Cited by 161
Zhang, Y., Park, D. S., Han, W., Qin, J., Gulati, A., Shor, J., Jansen, A., Xu, Y., Huang, Y., Wang, S., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3182537 · Cited by 161
EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model
Ji, X., Zhou, H., Wang, K., Wu, Q., Wu, W., Xu, F., & Cao, X. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings · doi:10.1145/3528233.3530745 · Cited by 155
Metadata
- Authors
- Cao, H., Cooper, D. G., Keutmann, M. K., Gur, R. C., Nenkova, A., & Verma, R.
- Venue
- IEEE Transactions on Affective Computing
- Published
- 2014-10-01
- Volume
- 5
- Issue
- 4
- Pages
- 377-390
- ISSN
- 1949-3045 (ISSN-L)
- OpenAlex
- W2030931454
- Cited by
- 914
- References
- 51
- FWCI
- 4.88
- Type
- journal article
- Language
- en
- Open access
- green
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- Cao, H., Cooper, D. G., Keutmann, M. K., Gur, R. C., Nenkova, A., & Verma, R. (2014). CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset. IEEE Transactions on Affective Computing. https://doi.org/10.1109/taffc.2014.2336244
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