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Nonlinguistic vocalizations from online amateur videos for emotion research : A validated corpus

Anikin, Andrey LU and Persson, Tomas LU (2016) In Behavior Research Methods p.1-14
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... (More)
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). (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Emotion, Nonlinguistic vocalizations, Naturalistic vocalizations, Acoustic analysis
in
Behavior Research Methods
pages
15 pages
publisher
The Psychonomic Society
external identifiers
  • Scopus:84964678013
ISSN
1554-3528
DOI
10.3758/s13428-016-0736-y
language
English
LU publication?
yes
id
f27de1dd-209b-444d-b160-2047bcaa33c0
alternative location
http://cogsci.se/personal/results/01_anikin-persson_2016_naturalistics-non-linguistic-vocalizations/anikin-persson_2016_naturalistic-non-linguistic-vocalizations_ver7.1.pdf
date added to LUP
2016-05-02 10:24:36
date last changed
2017-01-01 08:24:23
@article{f27de1dd-209b-444d-b160-2047bcaa33c0,
  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 &lt;40% for joy and pain, to &gt;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).},
  author       = {Anikin, Andrey and Persson, Tomas},
  issn         = {1554-3528},
  keyword      = {Emotion,Nonlinguistic vocalizations,Naturalistic vocalizations,Acoustic analysis},
  language     = {eng},
  month        = {04},
  pages        = {1--14},
  publisher    = {The Psychonomic Society},
  series       = {Behavior Research Methods},
  title        = {Nonlinguistic vocalizations from online amateur videos for emotion research : A validated corpus},
  url          = {http://dx.doi.org/10.3758/s13428-016-0736-y},
  year         = {2016},
}