Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph
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AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, Louis-Philippe Morency. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.
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CMU-MOSEI encompasses 3837 videos collected from over 1000 distinctive YouTube speakers, with a gender distribution of 57% male and 43% female speakers.
“This dataset encompasses 3837 videos collected from over 1000 distinctive YouTube speakers, with a reasonably balanced gender distribution of 57% male and 43% female speakers, highlighting the dataset’s focus on inclusivity.”
From A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023) · cites this paper as [30]
Section 3.1 (Data Acquisition; page unavailable) reports 3,228 videos after quality inspection, not 3,837, and identifies 1,000 YouTube identities rather than over 1,000 distinct speakers. It does report a 57% male to 43% female gender balance, based on judges’ annotations of the videos. (LLM, conf=0.90)
Experimental — accuracy not yet measuredCMU-MOSEI thematic diversity is spread across 250 different themes, with three predominant themes: comments (16.2%), debates (2.9%), and consultations (1.8%).
“The thematic diversity of the CMU-MOSEI dataset is notable, with a spread across 250 different themes. The three predominant themes are comments (16.2%), debates (2.9%), and consultations (1.8%), while the rest of the themes are nearly evenly distributed.”
From A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023) · cites this paper as [30]
The paper’s “Variety in annotations” section says CMU-MOSEI spans 250 topics. Section 3.1 (Data Acquisition) gives the same percentages and says the remaining topics are almost uniformly distributed, but names the leading topic as reviews (16.2%), not comments. It calls the other two debate (2.9%) and consulting (1.8%). (LLM, conf=0.90)
Experimental — accuracy not yet measuredCMU-MOSEI has annotations at the utterance level, with 23,259 samples annotated in this manner.
“One of CMU-MOSEI’s unique characteristics is its annotations at the utterance level, with 23,259 samples annotated in this manner.”
From A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023) · cites this paper as [30]
Section 3.2 (Annotation; page not available) says each sentence is annotated, supporting sentence-level annotations. But the dataset description and Table 1 report 23,453 annotated segments or data points, not 23,259; the cited paper also does not call them utterances. (LLM, conf=0.90)
Experimental — accuracy not yet measuredCMU-MOSEI is a crucial resource in multimodal sentiment analysis and emotion recognition, distinguished by its scale, which is unmatched in the field.
“The CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset [30] is a crucial resource in multimodal sentiment analysis and emotion recognition, distinguished by its scale, which is unmatched in the field.”
From A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023) · cites this paper as [30]
The authors generated open-ended AQAs about speech using the four datasets IEMOCAP, MOSEI, LibriTTS, and VoxCeleb2.
“We generate open-ended AQAs about speech using the four commonly used datasets IEMOCAP [23], MOSEI [24], LibriTTS [25], and Vox-Celeb2 [26,27].”
From Joint Audio and Speech Understanding (2023) · cites this paper as [24]
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- Authors
- Bagher Zadeh, A., Liang, P. P., Poria, S., Cambria, E., & Morency, L.-P.
- Affiliations
- California Miramar University (US)
- Nanyang Technological University (SG)
- Agency for Science, Technology and Research (SG)
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 2236-2246
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2883409523
- Cited by
- 1,433
- References
- 62
- Citations by year
- 2026: 281 · 2025: 300 · 2024: 266 · 2023: 202 · 2022: 153 · 2021: 113 · 2020: 73 · 2019: 39 · 2018: 6
- FWCI
- 17.41
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- CMU-MOSEI, multimodal fusion, emotion recognition
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