| Title |
Nonlinguistic vocalizations from online amateur videos for emotion research: A validated corpus
|
|---|---|
| Published in |
Behavior Research Methods, April 2016
|
| DOI | 10.3758/s13428-016-0736-y |
| Pubmed ID | |
| Authors |
Andrey Anikin, Tomas Persson |
| Abstract |
This study introduces a corpus of 260 naturalistic human nonlinguistic vocalizations representing nine emotions: amusement, anger, disgust, effort, fear, joy, pain, pleasure, and sadness. The recognition accuracy in a rating task varied greatly per emotion, from <40% for joy and pain, to >70% for amusement, pleasure, fear, and sadness. In contrast, the raters' linguistic-cultural group had no effect on recognition accuracy: The predominantly English-language corpus was classified with similar accuracies by participants from Brazil, Russia, Sweden, and the UK/USA. Supervised random forest models classified the sounds as accurately as the human raters. The best acoustic predictors of emotion were pitch, harmonicity, and the spacing and regularity of syllables. This corpus of ecologically valid emotional vocalizations can be filtered to include only sounds with high recognition rates, in order to study reactions to emotional stimuli of known perceptual types (reception side), or can be used in its entirety to study the association between affective states and vocal expressions (production side). |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | 1% |
| Unknown | 79 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 21% |
| Student > Master | 16 | 20% |
| Researcher | 7 | 9% |
| Student > Doctoral Student | 7 | 9% |
| Student > Bachelor | 7 | 9% |
| Other | 10 | 13% |
| Unknown | 16 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 25 | 31% |
| Computer Science | 7 | 9% |
| Agricultural and Biological Sciences | 6 | 8% |
| Neuroscience | 4 | 5% |
| Social Sciences | 4 | 5% |
| Other | 12 | 15% |
| Unknown | 22 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 May 2016.
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#20,655,488
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Outputs from Behavior Research Methods
#1,979
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Outputs of similar age from Behavior Research Methods
#17
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