EmotionTalk: An Interactive Chinese Multimodal Emotion Dataset With Rich Annotations

Introduction

EmotionTalk is an interactive Chinese multimodal emotion dataset with rich annotations. This dataset provides multimodal information from 19 actors participating in dyadic conversation settings, incorporating acoustic, visual, and textual modalities. It includes 23.6 hours of speech (19,250 utterances), annotations for 7 utterance-level emotion categories (happy, surprise, sad, disgust, anger, fear, and neutral), 5-dimensional sentiment labels (negative, weakly negative, neutral, weakly positive, and positive) and 4-dimensional speech captions (speaker, speaking style, emotion and overall). The dataset is released under a CC BY-NC-SA 4.0 license, meaning it is available for non-commercial use.

Dataset Details

This dataset contains 23.6 hours of spontaneous dialogue recordings. Key features of the dataset include:

• Speakers: 19 speakers.
• Audio Format: WAV files with a 44.1kHz sampling rate.
• Label: Happy, angry, sad, disgusted, fear, surprise, neutral.
• Annotations: The dataset includes annotations for each modality.
  • Text modality: data (each annotator's labeling results), emotion_result, speaker_id, file_name (file path), content (transcription).
  • Audio modality: data (each annotator's labeling results), emotion_result, speaker_id, paragraphs (timestamp), sourceAttr (caption), file_name (file path), content (transcription).
  • Video modality: data (each annotator's labeling results), emotion_result, speaker_id, file_name (file path).
  • Multimodal: data (each annotator's labeling results), emotion_result, Continuous label_result, speaker_id, file_name (file path).

Dataset Structure

The dataset file structure is as follows.

data
├── audio/*.tar  
├── Text/*.tar  
├── Video/*.tar  
└── Multimodal/*.tar  

Dataset Statistics

The dataset is split into three subsets:

	Angry	Disgusted	Fearful	Happy	Neutral	Sad	Surprised	Total
Train	2950	1142	672	2986	5377	919	1367	15413
Val(G01/G12)	409	95	125	360	675	111	133	1908
Test(G03/G15)	339	134	125	246	801	123	161	1929
Total	3698	1371	922	3592	6853	1153	1661	19250

For more details, please refer to our paper EmotionTalk.

📚 Cite me

@article{sun2025emotiontalk,
  title={EmotionTalk: An Interactive Chinese Multimodal Emotion Dataset With Rich Annotations},
  author={Sun, Haoqin and Wang, Xuechen and Zhao, Jinghua and Zhao, Shiwan and Zhou, Jiaming and Wang, Hui and He, Jiabei and Kong, Aobo and Yang, Xi and Wang, Yequan and others},
  journal={arXiv preprint arXiv:2505.23018},
  year={2025}
}