MER2023: Multimodal Emotion Recognition Challenge Dataset

MER2023 is a large-scale multimodal emotion recognition dataset created by MERChallenge for a research competition. The dataset is designed to address challenges in real-world deployment, such as costly labeling and modality-related issues. It was last updated on November 8, -2025.

Use Cases

• Training multimodal emotion recognition models based on the challenge's focus on multiple data types.
• Benchmarking deep learning architectures for emotion recognition based on the dataset's large-scale nature.
• Researching solutions for real-world deployment challenges like costly labeling and modality issues mentioned in the description.

Strengths

• Dataset is associated with a formal research challenge, suggesting a benchmark purpose.
• Focuses on multimodal data, which is indicated by the platform tags and description.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

MERChallenge

Collection Method

Likely collected and annotated for a research competition.

Time Range

null

Freshness

Last updated 2025-11-08 05:26:59; freshness should be verified.

Geography

null