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1.	Anikin, Andrey, et al. (författare) Nonlinguistic vocalizations from online amateur videos for emotion research : A validated corpus 2017 Ingår i: Behavior Research Methods. - : Springer Science and Business Media LLC. - 1554-3528. ; 49:2, s. 758-771 Tidskriftsartikel (refereegranskat)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).

Anikin, Andrey, et al. (författare)
Nonlinguistic vocalizations from online amateur videos for emotion research : A validated corpus
2017
Ingår i: Behavior Research Methods. - : Springer Science and Business Media LLC. - 1554-3528. ; 49:2, s. 758-771
Tidskriftsartikel (refereegranskat)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).

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