Nonlinguistic vocalizations from online amateur videos for emotion research : A validated corpus
Author
Summary, in English
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).
Department/s
- Cognitive Science
- LUCS Cognitive Zoology Group
- Department of Philosophy
Publishing year
2017-04-29
Language
English
Pages
758-771
Publication/Series
Behavior Research Methods
Volume
49
Issue
2
Links
Document type
Journal article
Publisher
Springer
Topic
- Psychology (excluding Applied Psychology)
Keywords
- Emotion
- Nonlinguistic vocalizations
- Naturalistic vocalizations
- Acoustic analysis
Status
Published
Research group
- LUCS Cognitive Zoology Group
ISBN/ISSN/Other
- ISSN: 1554-3528