---
title: "Speech emotion (IEMOCAP, RAVDESS): tasks and access"
description: "Speech emotion (IEMOCAP, RAVDESS): compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/speech-emotion-iemocap-ravdess
markdown_url: https://scholar4ai.com/datasets/speech-emotion-iemocap-ravdess.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Speech emotion (IEMOCAP, RAVDESS): compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Speech emotion (IEMOCAP, RAVDESS)

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/6.md)

Speech, speaker and sound collections.

- data category: Speech and audio benchmarks
- 0 papers in the last 7 days
- Names searched for: IEMOCAP, RAVDESS

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Speech and audio benchmarks datasets: https://scholar4ai.com/datasets/speech-and-audio-benchmarks.md

## Tasks, access and usage conditions

Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.

*Dataset comparison: tasks, access and usage conditions*

| Dataset / version | Task and evaluation | Access and preparation | Usage conditions |
|---|---|---|---|
| IEMOCAP | Acted/improvised dyadic emotional speech, with audiovisual and motion-capture data; about 12 hours. Evaluation: Use speaker/session-disjoint evaluation, specify whether happy/excited are merged, and report weighted/unweighted accuracy. | [Official source / documentation](https://sail.usc.edu/iemocap/) Request the release through USC SAIL and specify the label mapping and modalities used. | Obtain the corpus under USC’s release agreement; public documentation is not unrestricted permission to redistribute recordings. |
| RAVDESS 1.0.0 | Acted emotional speech/song from 24 actors, distributed in audio and video variants. Evaluation: Split by actor and identify modality, intensity and labels; random utterance splits can leak the same speaker into testing. | [Official source / documentation](https://zenodo.org/records/1188976) Download the required audio-speech, song or audiovisual files from the creator’s Zenodo record. | The record’s explicit license section specifies CC BY-NC-SA 4.0; commercial licensing is offered separately. |

Guide updated 2026-11-05.

## Key findings

- From Nov 2023 to Oct 2026, 110 archived papers mentioned Speech emotion (IEMOCAP, RAVDESS) in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jan 2026 (6).
- Speech emotion (IEMOCAP, RAVDESS) was mentioned in 2 papers in the last 30 days (2026-10-07 to 2026-11-05), ranking #127 of the 246 datasets mentioned.
- By journal category: AI (2).
- Most of them appeared in Language Resources and Evaluation (1) and Artificial Intelligence (1).
- From 2017 to 2026 (so far), Speech emotion (IEMOCAP, RAVDESS) was mentioned in 160 papers in the tracked journals, most of them in AI (153), information systems (5) and statistics (1).
- 33 of them were published in 2025, against 1 in 2017.
- Since 2023, the institutions with the most of these papers were Chinese Academy of Sciences (5), Central South University of Forestry and Technology (4) and Chongqing University of Posts and Telecommunications (4).
- The journals publishing most of them were Speech Communication (11), Computer Speech & Language (10) and Knowledge-Based Systems (10).
- The most-cited of them is “Evaluating deep learning architectures for Speech Emotion Recognition” (Neural Networks, 2017), with 554 citations.
- Papers use Speech emotion (IEMOCAP, RAVDESS) most often with YouTube / TikTok (18), Facial expression (FER2013, AffectNet, RAF-DB) (9) and Twitter / X (9); it has 11 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 33 | 33 | 44 | 110 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 1 | 0 | 1 | 9 | 7 | 16 | 20 | 33 | 31 | 35 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 3 |
| [Statistics](https://scholar4ai.com/categories/statistics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| All categories | 1 | 0 | 1 | 9 | 7 | 17 | 21 | 33 | 33 | 38 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [Speech Communication](https://scholar4ai.com/journals/speech-communication.md) | 11 |
| 2 | [Computer Speech & Language](https://scholar4ai.com/journals/computer-speech-language.md) | 10 |
| 3 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 10 |
| 4 | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 9 |
| 5 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 8 |
| 6 | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 8 |
| 7 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 7 |
| 8 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 7 |
| 9 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 6 |
| 10 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 6 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 5 |
| 2 | Central South University of Forestry and Technology | 4 |
| 3 | [Chongqing University of Posts and Telecommunications](https://scholar4ai.com/institutions/chongqing-university-of-posts-and-telecommunications.md) | 4 |
| 4 | Hebei University of Science and Technology | 4 |
| 5 | [Institute of Automation](https://scholar4ai.com/institutions/institute-of-automation.md) | 4 |
| 6 | Mohamed bin Zayed University of Artificial Intelligence | 4 |
| 7 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 4 |
| 8 | SUNY New Paltz | 4 |
| 9 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 4 |
| 10 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 4 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 63 |
| 2 | India | 11 |
| 3 | United States | 10 |
| 4 | Australia | 7 |
| 5 | Canada | 7 |
| 6 | United Kingdom | 7 |
| 7 | United Arab Emirates | 6 |
| 8 | France | 6 |
| 9 | Spain | 5 |
| 10 | South Korea | 5 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | IEMOCAP | 63 |
| 2 | speech emotion recognition | 39 |
| 3 | multimodal emotion recognition | 37 |
| 4 | MELD | 34 |
| 5 | [multimodal fusion](https://scholar4ai.com/keywords/multimodal-fusion.md) | 24 |
| 6 | emotion recognition in conversation | 22 |
| 7 | RAVDESS | 14 |
| 8 | emotion recognition | 13 |
| 9 | EMO-DB | 11 |
| 10 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 10 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 554 | 4.8 | [Evaluating deep learning architectures for Speech Emotion Recognition](https://doi.org/10.1016/j.neunet.2017.02.013) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 2017-03-21 | Haytham M. Fayek, Margaret Lech, Lawrence Cavedon |
| 2 | 411 | 9.5 | [Dawn of the Transformer Era in Speech Emotion Recognition: Closing the Valence Gap](https://doi.org/10.1109/tpami.2023.3263585) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2023-03-31 | Johannes Wagner, Andreas Triantafyllopoulos, Hagen Wierstorf et al. |
| 3 | 248 | 2.8 | [Bagged support vector machines for emotion recognition from speech](https://doi.org/10.1016/j.knosys.2019.104886) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2019-08-02 | Anjali Bhavan, Pankaj Chauhan, Hitkul et al. |
| 4 | 239 | 4.3 | [Deep learning based multimodal emotion recognition using model-level fusion of audio–visual modalities](https://doi.org/10.1016/j.knosys.2022.108580) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2022-03-16 | Asif Iqbal Middya, Baibhav Nag, Sarbani Roy |
| 5 | 205 | 2.8 | [MLT-DNet: Speech emotion recognition using 1D dilated CNN based on multi-learning trick approach](https://doi.org/10.1016/j.eswa.2020.114177) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2020-10-31 | Mustaqeem Mustaqeem, Soonil Kwon |

## Used together with

Papers use Speech emotion (IEMOCAP, RAVDESS) most often with YouTube / TikTok (18), Facial expression (FER2013, AffectNet, RAF-DB) (9) and Twitter / X (9); it has 11 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use Speech emotion (IEMOCAP, RAVDESS) together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [YouTube / TikTok](https://scholar4ai.com/datasets/youtube-tiktok.md) | Dataset | 18 | 2.9 |
| [Facial expression (FER2013, AffectNet, RAF-DB)](https://scholar4ai.com/datasets/facial-expression-fer2013-affectnet-raf-db.md) | Dataset | 9 | 35.4 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 9 | 0.6 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 8 | 1.0 |
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | Dataset | 7 | 0.7 |
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 5 | 3.2 |
| [Amazon Mechanical Turk](https://scholar4ai.com/datasets/amazon-mechanical-turk.md) | Dataset | 4 | 2.0 |
| [MODIS](https://scholar4ai.com/datasets/modis.md) | Dataset | 4 | 2.9 |
| [LibriSpeech](https://scholar4ai.com/datasets/librispeech.md) | Dataset | 3 | 14.7 |
| [VoxCeleb](https://scholar4ai.com/datasets/voxceleb.md) | Dataset | 3 | 25.6 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 3 | 0.9 |

The whole network: https://scholar4ai.com/code/network.md

## Papers

*Papers mentioning Speech emotion (IEMOCAP, RAVDESS) in the last 30 days, newest first.*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-25 | [EGY-MER: Establishing The First Egyptian Arabic Multimodal Emotion Recognition Dataset for Affective Computing](https://doi.org/10.1007/s10579-026-09941-9) | [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | Amany Hussein, May Hussien, Ammar Hassona et al. | German International University; German University in Cairo; Universität Ulm |
| 2026-10-16 | [ITEACH-Net: Inverted teacher-student search network for emotion recognition in conversation](https://doi.org/10.1016/j.artint.2026.104643) | [Artificial Intelligence](https://scholar4ai.com/journals/artificial-intelligence.md) | Haiyang Sun, Zheng Lian, Chenglong Wang et al. | Chinese Academy of Sciences; Institute of Automation; University of Chinese Academy of Sciences; … |

## Cite this page

Scholar4AI. (2026). Speech emotion (IEMOCAP, RAVDESS). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/speech-emotion-iemocap-ravdess

```bibtex
@misc{scholar4ai-datasets-speech-emotion-iemocap-ravdess,
  author = {{Scholar4AI}},
  title = {Speech emotion (IEMOCAP, RAVDESS)},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/speech-emotion-iemocap-ravdess}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).