AbstractTTS/IEMOCAP dataset hosted on Hugging Face and contributed by the HF Datasets community

Processed IEMOCAP dataset released by Dai, W., Zheng, D., Yu, F., Zhang, Y., & Hou, Y. (2025, February 12). A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion. arXiv.org. https://arxiv.org/abs/2502.08573

Dataset Card for "IEMOCAP_Text"

This dataset obtained from IEMOCAP dataset. For more information go to IEMOCAP webpage. This dataset contains 5 most common classes includes angry, happy, excitement, neutral and sad. Based on articles in this field, we merge excitement and happy classes. Our dataset contaions 5531 utterances and it splits based on the sessions. More Information needed

Summary of previous research on video-based emotion recognition using IEMOCAP database.

Test splits for the categorical emotion datasets CREMA-D, emoDB, IEMOCAP, MELD, RAVDESS used inside audEERING.

For each dataset, a CSV file is provided listing the file names included in the test split.

The test splits were designed trying to balance gender and emotional categories as good as possible.

The Interactive Emotional Dyadic Motion Capture (IEMOCAP) database is an acted, multimodal and multispeaker database, recently collected at SAIL lab at USC. It contains approximately 12 hours of audiovisual data, including video, speech, motion capture of face, text transcriptions. It consists of dyadic sessions where actors perform improvisations or scripted scenarios, specifically selected to elicit emotional expressions. IEMOCAP database is annotated by multiple annotators into categorical labels, such as anger, happiness, sadness, neutrality, as well as dimensional labels such as valence, activation and dominance. The detailed motion capture information, the interactive setting to elicit authentic emotions, and the size of the database make this corpus a valuable addition to the existing databases in the community for the study and modeling of multimodal and expressive human communication.

Statistics of the MELD, EmoryNLP, DailyDialog, and IEMOCAP.

Dataset

This dataset was created by Rumaiya

Contents

Training and test instances for the IEMOCAP corpus.

Dataset

This dataset was created by AyaOsama21

Contents

audio statistics

mteb/iemocap dataset hosted on Hugging Face and contributed by the HF Datasets community

Recalls for speech emotion recognition using IEMOCAP and DNN.

Precision of speech emotion recognition using IEMOCAP and CNN.

F1-scores for speech emotion recognition using a common model set and CNN.

cairocode/IEMOCAP dataset hosted on Hugging Face and contributed by the HF Datasets community

Confusion matrix [%] using IEMOCAP and DNN with MFCC/SDC features.

WiktorJakubowski/IEMOCAP dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Bảo Xuyên Nguyễn Lê

Released under Apache 2.0

Contents

Processing Pipeline

• Transcription: Generated using OpenAI Whisper speech recognition model

• VAD Scoring: - VAD Scoring: Voice Activity Detection and emotion dimensionality (Valence, Arousal, Dominance) computed using audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim model.

• Metadata: Includes audio duration, transcript length, character counts, and VAD scores

Data Format

An example of a field in the metadata file is as follows { "file_id": "emovdb_amused_1-15_0001_1933", "original_path": "..\data_collection\tts_data\processed\emovdb\amused_1-15_0001.wav", "dataset": "emovdb", "status": "success", "error": null, "processed_audio_path": "None", "transcript_path": "processed_datasets\transcripts\emovdb_amused_1-15_0001_1933.json", "vad_path": "processed_datasets\vad_scores\emovdb_amused_1-15_0001_1933.json", "text": "Author of the Danger Trail, Phillips Deals, etc.", "language": "en", "audio_duration": 4.384671201814059, "text_length": 48, "valence": 0.7305971384048462, "arousal": 0.704948365688324, "dominance": 0.6887099146842957, "vad_confidence": 0.9830486676764241 },

License and Attribution

This work is licensed under CC BY-NC-SA 4.0.

Required Citations: - CREMA-D: 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, 5(4), 377-390.

• EmoV-DB: Adigwe, A., Tits, N., Haddad, K. E., Ostadabbas, S., & Dutoit, T. (2018). The emotional voices database: Towards controlling the emotion dimension in voice generation systems. arXiv preprint arXiv:1806.09514.

• IEMOCAP: Busso, C., Bulut, M., Lee, C. C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J. N., Lee, S., & Narayanan, S. S. (2008). IEMOCAP: Interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42(4), 335-359.

• RAVDESS: Livingstone, S. R., & Russo, F. A. (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE, 13(5), e0196391.

Limitations

• Non-commercial use only
• Subject to the licensing terms of source datasets