Dataset Information

Superior Communication of Positive Emotions Through Nonverbal Vocalisations Compared to Speech Prosody.

ABSTRACT: The human voice communicates emotion through two different types of vocalizations: nonverbal vocalizations (brief non-linguistic sounds like laughs) and speech prosody (tone of voice). Research examining recognizability of emotions from the voice has mostly focused on either nonverbal vocalizations or speech prosody, and included few categories of positive emotions. In two preregistered experiments, we compare human listeners' (total n = 400) recognition performance for 22 positive emotions from nonverbal vocalizations (n = 880) to that from speech prosody (n = 880). The results show that listeners were more accurate in recognizing most positive emotions from nonverbal vocalizations compared to prosodic expressions. Furthermore, acoustic classification experiments with machine learning models demonstrated that positive emotions are expressed with more distinctive acoustic patterns for nonverbal vocalizations as compared to speech prosody. Overall, the results suggest that vocal expressions of positive emotions are communicated more successfully when expressed as nonverbal vocalizations compared to speech prosody.

Supplementary information

The online version contains supplementary material available at 10.1007/s10919-021-00375-1.

SUBMITTER: Kamiloglu RG

PROVIDER: S-EPMC8553689 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Superior Communication of Positive Emotions Through Nonverbal Vocalisations Compared to Speech Prosody.

Kamiloğlu Roza G RG Boateng George G Balabanova Alisa A Cao Chuting C Sauter Disa A DA

Journal of nonverbal behavior 20210724 4

The human voice communicates emotion through two different types of vocalizations: nonverbal vocalizations (brief non-linguistic sounds like laughs) and speech prosody (tone of voice). Research examining recognizability of emotions from the voice has mostly focused on either nonverbal vocalizations or speech prosody, and included few categories of positive emotions. In two preregistered experiments, we compare human listeners' (total <i>n</i> = 400) recognition performance for 22 positive emotio ...[more]

PMID: 34744232

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Project description:BackgroundCommunication is a critical component of the patient-provider relationship; however, limited research exists on the role of nonverbal communication. Virtual human training is an informatics-based educational strategy that offers various benefits in communication skill training directed at providers. Recent informatics-based interventions aimed at improving communication have mainly focused on verbal communication, yet research is needed to better understand how virtual humans can improve verbal and nonverbal communication and further elucidate the patient-provider dyad.ObjectiveThe purpose of this study is to enhance a conceptual model that incorporates technology to examine verbal and nonverbal components of communication and develop a nonverbal assessment that will be included in the virtual simulation for further testing.MethodsThis study will consist of a multistage mixed methods design, including convergent and exploratory sequential components. A convergent mixed methods study will be conducted to examine the mediating effects of nonverbal communication. Quantitative (eg, MPathic game scores, Kinect nonverbal data, objective structured clinical examination communication score, and Roter Interaction Analysis System and Facial Action Coding System coding of video) and qualitative data (eg, video recordings of MPathic-virtual reality [VR] interventions and student reflections) will be collected simultaneously. Data will be merged to determine the most crucial components of nonverbal behavior in human-computer interaction. An exploratory sequential design will proceed, consisting of a grounded theory qualitative phase. Using theoretical, purposeful sampling, interviews will be conducted with oncology providers probing intentional nonverbal behaviors. The qualitative findings will aid the development of a nonverbal communication model that will be included in a virtual human. The subsequent quantitative strand will incorporate and validate a new automated nonverbal communication behavior assessment into the virtual human simulation, MPathic-VR, by assessing interrater reliability, code interactions, and dyadic data analysis by comparing Kinect responses (system recorded) to manually scored records for specific nonverbal behaviors. Data will be integrated using building integration to develop the automated nonverbal communication behavior assessment and conduct a quality check of these nonverbal features.ResultsSecondary data from the MPathic-VR randomized controlled trial data set (210 medical students and 840 video recordings of interactions) were analyzed in the first part of this study. Results showed differential experiences by performance in the intervention group. Following the analysis of the convergent design, participants consisting of medical providers (n=30) will be recruited for the qualitative phase of the subsequent exploratory sequential design. We plan to complete data collection by July 2023 to analyze and integrate these findings.ConclusionsThe results from this study contribute to the improvement of patient-provider communication, both verbal and nonverbal, including the dissemination of health information and health outcomes for patients. Further, this research aims to transfer to various topical areas, including medication safety, informed consent processes, patient instructions, and treatment adherence between patients and providers.International registered report identifier (irrid)DERR1-10.2196/46601.

Dataset Information

Superior Communication of Positive Emotions Through Nonverbal Vocalisations Compared to Speech Prosody.

Supplementary information

Publications

Superior Communication of Positive Emotions Through Nonverbal Vocalisations Compared to Speech Prosody.

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