Emotion appraisal dimensions inferred from vocal expressions are consistent across cultures: a comparison between Australia and India

Nordström, Henrik; Laukka, Petri; Thingujam, Nutankumar S.; Schubert, Emery; Elfenbein, Hillary Anger

doi:10.1098/rsos.170912

Cited by 20 publications

(12 citation statements)

References 38 publications

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“…However, while durational feature contributed to a larger extent in the native accent, the mean and range of speech intensity contributed more in the foreign and regional accent in differentiating the confidence and doubt. These findings highlight computational mechanisms underlying inter-cultural/group confidence perception in speech communication and lend support to the dialect theory of vocal expression recognition [17][18][19][20][21].…”

Section: Discussionsupporting

confidence: 68%

Predicting confidence and doubt in accented speakers: Human perception and machine learning experiments

Jiang¹,

Pell²

2018

Speech Prosody 2018

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Speech prosody provides salient and reliable cues to facilitate social communication. What computational mechanism underlies social judgment towards "out-group" speakers is unclear. This paper focused on Speaker Confidence, a factor affecting one's trustworthiness, persuasiveness and feeling of (un)knowing, and Speaker Accent, a factor marking one's identity. We demonstrate that native Canadian-English listeners can recognize confident and doubtful expressions in foreign-and regional-accented speakers. A stronger impression of confidence was shown towards the native speakers. The acoustic analysis demonstrated that speakers systematically varied the mean fundamental frequency to indicate confident and doubt regardless of accent. The outgroup speakers varied more on intensity height and variation to achieve certain level of confidence. Machine learning experiments showed above-chance accuracies in all accents to classify vocal expression based on global acoustic cues, highlighting the role of acoustic regularities at utterance level in confidence encoding. Moreover, the classification rate was higher when the model trained in native accent was tested on the native than the regional accent, highlighting an in-group bias of predicting novel vocal expression of confidence from acoustic cues. These findings lend support to the dialect theory of vocal expression recognition while demonstrating a computational mechanism underlying inter-cultural/intergroup confidence perception via speech prosody.

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Section: Discussionsupporting

confidence: 68%

Predicting confidence and doubt in accented speakers: Human perception and machine learning experiments

Jiang¹,

Pell²

2018

Speech Prosody 2018

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“…However, a couple of studies that used rating scales or free responses were included because they also provided data on the proportion of accurate responses (Abelin & Allwood, 2000; Gendron, Roberson, van der Vyver, & Barrett, 2014; Huang, Erickson, & Akagi, 2008). The accuracy criterion excluded studies that investigated emotion recognition using dimensional ratings (e.g., activation, valence, emotion intensity, appraisal dimensions)—which do not provide measures of accuracy (e.g., Koeda et al, 2013; Nordström, Laukka, Thingujam, Schubert, & Elfenbein, 2017; Pfitzinger, Amir, Mixdorff, & Bösel, 2011).…”

Section: Methodsmentioning

confidence: 99%

Cross-Cultural Emotion Recognition and In-Group Advantage in Vocal Expression: A Meta-Analysis

2020

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Most research on cross-cultural emotion recognition has focused on facial expressions. To integrate the body of evidence on vocal expression, we present a meta-analysis of 37 cross-cultural studies of emotion recognition from speech prosody and nonlinguistic vocalizations, including expressers from 26 cultural groups and perceivers from 44 different cultures. Results showed that a wide variety of positive and negative emotions could be recognized with above-chance accuracy in cross-cultural conditions. However, there was also evidence for in-group advantage with higher accuracy in within- versus cross-cultural conditions. The distance between expresser and perceiver culture, measured via Hofstede’s cultural dimensions, was negatively correlated with recognition accuracy and positively correlated with in-group advantage. Results are discussed in relation to the dialect theory of emotion.

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“…A more typical approach has been to match vocal bursts to discrete emotions, using words or brief stories depicting antecedents (Cordaro et al, 2016; Simon-Thomas et al, 2009). More recently, investigators have been gathering ratings not only of emotion categories, but also inferred appraisals and intentions (Nordström, Laukka, Thingujam, Schubert, & Elfenbein, 2017; Shuman et al, 2017). By combining such data with statistical approaches developed in the study of emotional experience (Cowen & Keltner, 2017), we can compare how emotion categories and affect appraisals shape emotion recognition.…”

Section: The Present Investigationmentioning

confidence: 99%

Mapping 24 emotions conveyed by brief human vocalization.

Cowen¹,

Elfenbein²,

Laukka³

et al. 2019

American Psychologist

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145

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Emotional vocalizations are central to human social life. Recent studies have documented that people recognize at least 13 emotions in brief vocalizations. This capacity emerges early in development, is preserved in some form across cultures, and informs how people respond emotionally to music. What is poorly understood is how emotion recognition from vocalization is structured within what we call a semantic space, the study of which addresses questions critical to the field: How many distinct kinds of emotions can be expressed? Do expressions convey emotion categories or affective appraisals (e.g., valence, arousal)? Is the recognition of emotion expressions discrete or continuous? Guided by a new theoretical approach to emotion taxonomies, we apply large-scale data collection and analysis techniques to judgments of 2032 emotional vocal bursts produced in laboratory settings (Study 1) and 48 found in the real world (Study 2) by U.S. English speakers (n = 1105). We find that vocal bursts convey at least 24 distinct kinds of emotion. Emotion categories (sympathy, awe) more so than affective appraisals (including valence and arousal) organize emotion recognition. In contrast to discrete emotion theories, the emotion categories conveyed by vocal bursts are bridged by smooth gradients with continuously varying meaning. We visualize the complex, high-dimensional space of emotion conveyed by brief human vocalization within an online interactive map: https://s3-us-west-1.amazonaws.com/vocs/map.html.

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Emotion appraisal dimensions inferred from vocal expressions are consistent across cultures: a comparison between Australia and India

Cited by 20 publications

References 38 publications

Predicting confidence and doubt in accented speakers: Human perception and machine learning experiments

Predicting confidence and doubt in accented speakers: Human perception and machine learning experiments

Cross-Cultural Emotion Recognition and In-Group Advantage in Vocal Expression: A Meta-Analysis

Mapping 24 emotions conveyed by brief human vocalization.

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