Nonlinguistic vocalizations from online amateur videos for emotion research : A validated corpus
Anikin, Andrey LU
and Persson, Tomas
LU
(2017)
In Behavior Research Methods
49(2).
p.758-771
- 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:
https://lup.lub.lu.se/record/f27de1dd-209b-444d-b160-2047bcaa33c0
- author
- Anikin, Andrey
LU
and Persson, Tomas
LU
- organization
- publishing date
- 2017-04-29
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Emotion, Nonlinguistic vocalizations, Naturalistic vocalizations, Acoustic analysis
- in
- Behavior Research Methods
- volume
- 49
- issue
- 2
- pages
- 758 - 771
- publisher
- Springer
- external identifiers
-
- scopus:84964678013
- pmid:27130172
- wos:000400391800027
- 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/publications/anikin-persson_2017_nonlinguistic-vocs/anikin-persson_2017_nonlinguistic-vocs.pdf
- date added to LUP
- 2016-05-02 10:24:36
- date last changed
- 2022-04-16 08:58:00
@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 <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).}},
author = {{Anikin, Andrey and Persson, Tomas}},
issn = {{1554-3528}},
keywords = {{Emotion; Nonlinguistic vocalizations; Naturalistic vocalizations; Acoustic analysis}},
language = {{eng}},
month = {{04}},
number = {{2}},
pages = {{758--771}},
publisher = {{Springer}},
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}},
doi = {{10.3758/s13428-016-0736-y}},
volume = {{49}},
year = {{2017}},
}