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

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Nonlinguistic vocalizations from online amateur videos for emotion research : A validated corpus. / Anikin, Andrey; Persson, Tomas.

In: Behavior Research Methods, Vol. 49, No. 2, 29.04.2017, p. 758-771.

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TY - JOUR

T1 - Nonlinguistic vocalizations from online amateur videos for emotion research

T2 - A validated corpus

AU - Anikin, Andrey

AU - Persson, Tomas

PY - 2017/4/29

Y1 - 2017/4/29

N2 - 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).

AB - 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).

KW - Emotion

KW - Nonlinguistic vocalizations

KW - Naturalistic vocalizations

KW - Acoustic analysis

U2 - 10.3758/s13428-016-0736-y

DO - 10.3758/s13428-016-0736-y

M3 - Article

C2 - 27130172

VL - 49

SP - 758

EP - 771

JO - Behavior Research Methods

JF - Behavior Research Methods

SN - 1554-3528

IS - 2

ER -