The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English

Abstract

The RAVDESS is a validated multimodal database of emotional speech and song. The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of 7356 recordings were each rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity and test-retest intrarater reliability were reported. Corrected accuracy and composite "goodness" measures are presented to assist researchers in the selection of stimuli. All recordings are made freely available under a Creative Commons license and can be downloaded at https://doi.org/10.5281/zenodo.1188976.

References 194

1. 1.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

2. 2.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

3. 3.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

4. 4.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

5. 5.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

6. 6.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

7. 7.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

8. 8.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

9. 9.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

10. 10.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

11. 11.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

12. 12.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

13. 13.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

14. 14.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

15. 15.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

16. 16.
    Hidden multiplicity in exploratory multiway ANOVA: Prevalence and remedies · 2015

    No notice
17. 17.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

18. 18.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

19. 19.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

20. 20.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

21. 21.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

22. 22.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

23. 23.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

24. 24.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

25. 25.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

26. 26.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

27. 27.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

28. 28.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

29. 29.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

30. 30.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

31. 31.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

32. 32.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

33. 33.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

34. 34.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

35. 35.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

36. 36.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

37. 37.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

38. 38.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

39. 39.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

40. 40.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

Cited by papers we hold 5

• Identifying the evidence of speech emotional dialects using artificial intelligence: A cross-cultural study. · 2022
  Not checked
• Speech Emotion Recognition by Late Fusion for Bidirectional Reservoir Computing With Random Projection · 2021
  Not checked
• An Enhanced Emotion Recognition Algorithm Using Pitch Correlogram, Deep Sparse Matrix Representation and Random Forest Classifier · 2021
  Not checked
• Efficient Feature-Aware Hybrid Model of Deep Learning Architectures for Speech Emotion Recognition · 2021
  Not checked
• COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings · 2020
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.