The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
M3ER: Multiplicative Multimodal Emotion Recognition using Facial, Textual, and Speech Cues
Open accessAbstract
We present M3ER, a learning-based method for emotion recognition from multiple input modalities. Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities. M3ER models a novel, data-driven multiplicative fusion method to combine the modalities, which learn to emphasize the more reliable cues and suppress others on a per-sample basis. By introducing a check step which uses Canonical Correlational Analysis to differentiate between ineffective and effective modalities, M3ER is robust to sensor noise. M3ER also generates proxy features in place of the ineffectual modalities. We demonstrate the efficiency of our network through experimentation on two benchmark datasets, IEMOCAP and CMU-MOSEI. We report a mean accuracy of 82.7% on IEMOCAP and 89.0% on CMU-MOSEI, which, collectively, is an improvement of about 5% over prior work.
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Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
IEMOCAP: Interactive emotional dyadic motion capture database
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Emotion recognition in human-computer interaction
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Relations Between Two Sets of Variates
Hotelling, H. (1936) · Biometrika · doi:10.2307/2333955 · Cited by 1,645
Deep Canonical Correlation Analysis
Andrew, G., Arora, R., Bilmes, J., & Livescu, K. (2013) · Cited by 1,602
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
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Cited by
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues
Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413570 · Cited by 319
Self-citationA Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face
Lian, H., Lu, C., Li, S., Zhao, Y., Tang, C., & Zong, Y. (2023) · Entropy · doi:10.3390/e25101440 · Cited by 224
Perception and deception: Exploring individual responses to deepfakes across different modalities
Ahmed, S., & Chua, H. W. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20383 · Cited by 29
Gupta, S., Kumar, P., & Tekchandani, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18021-x · Cited by 14
State-of-the-art Multimodal Emotion Recognition: A comprehensive survey and taxonomy
Yazici, A., Kucukyilmaz, T., Dokeroglu, T., Sharipbay, A., Lee, M.-H., & Tyler, B. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200642 · Cited by 11
Metadata
- Authors
- Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 02
- Pages
- 1359-1367
- ISSN
- 2374-3468
- 2159-5399
- OpenAlex
- W2997258743
- Cited by
- 253
- References
- 33
- FWCI
- 33.08
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.aaai.org/ (unspecified)
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- Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020). M3ER: Multiplicative Multimodal Emotion Recognition using Facial, Textual, and Speech Cues. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v34i02.5492
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