Predicting Well-being Using Short Ecological Momentary Audio Recordings

Huang, Yu-Ning; Zhao, Siyan; Rivera, Michael L.; Hong, Jason I.; Kraut, Robert E.

doi:10.1145/3411763.3451629

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…To predict well-being in the context of depression, the researchers [43][44][45] used speech features obtained from audio and video, such as, interviews and reading tasks. Huang et al [46], Kim et al [47] focused specifically on speech features. Regarding work-related environments, Kuutila et al [48] used software repositories to predict well-being without collecting audio data.…”

Section: Individual Well-being Data Analysismentioning

confidence: 99%

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Zeulner,

Hagerer,

Müller

et al. 2024

Information

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Current methods for assessing individual well-being in team collaboration at the workplace often rely on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal was to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We focus on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to explore which features are predictors of self-reported satisfaction scores. We applied the toolchain to a case study, where we collected videos of 20 teams with 56 participants collaborating over a four-day period in a team project in an educational environment. Our audiovisual speaker diarization extracted individual speech features from a noisy environment. As the dependent variable, team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores were predicted using speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain was able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

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Section: Individual Well-being Data Analysismentioning

confidence: 99%

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Zeulner,

Hagerer,

Müller

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…Regarding speech features, to predict well-being in the context of depression, researchers [32][33][34] used speech features obtained from audio and video, such as, interviews and reading tasks. Huang et al [35], Kim et al [36] focused specifically on speech features. Regarding work-related environments, Kuutila et al [37] used software repositories to predict well-being without collecting audio data.…”

Section: Individual Well-being Data Analysismentioning

confidence: 99%

Predicting Individual Well-Being in Teamwork Contexts Based on Multi-Modal Speech Features

Zeulner,

Hagerer,

Mueller

et al. 2024

Preprint

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Current methods for assessing individual well-being in team collaboration at the workplace rely often on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal is to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We are focusing on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to find which features are predictors of self-reported satisfaction scores. We apply the toolchain to a case study where we collected videos of 22 teams with 56 participants collaborating over a four-day period in a team project. Our audiovisual speaker diarization extracts individual speech features in a noisy environment. As the dependent variable team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores have been predicted with speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain is able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

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“…Some of the most widely available instruments to mitigate such risks are online and mobile services that offer quick screening tests of subjective well-being and mental health states and automatically generate respective recommendations. More than 240 mental health apps are available in the App Store today, some of which are extensively using machine learning for classifying and scoring their users in terms of their psychological or mental conditions [ 7 – 9 ]. Such apps attract consumers concerned with their psychological states, while these concerns are usually associated with higher risks for users’ SWB or mental health.…”

Section: Introductionmentioning

confidence: 99%

Predicting subjective well-being in a high-risk sample of Russian mental health app users

Panicheva¹,

Mararitsa²,

Sorokin³

et al. 2022

EPJ Data Sci.

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Despite recent achievements in predicting personality traits and some other human psychological features with digital traces, prediction of subjective well-being (SWB) appears to be a relatively new task with few solutions. COVID-19 pandemic has added both a stronger need for rapid SWB screening and new opportunities for it, with online mental health applications gaining popularity and accumulating large and diverse user data. Nevertheless, the few existing works so far have aimed at predicting SWB, and have done so only in terms of Diener’s Satisfaction with Life Scale. None of them analyzes the scale developed by the World Health Organization, known as WHO-5 – a widely accepted tool for screening mental well-being and, specifically, for depression risk detection. Moreover, existing research is limited to English-speaking populations, and tend to use text, network and app usage types of data separately. In the current work, we cover these gaps by predicting both mentioned SWB scales on a sample of Russian mental health app users who represent a population with high risk of mental health problems. In doing so, we employ a unique combination of phone application usage data with private messaging and networking digital traces from VKontakte, the most popular social media platform in Russia. As a result, we predict Diener’s SWB scale with the state-of-the-art quality, introduce the first predictive models for WHO-5, with similar quality, and reach high accuracy in the prediction of clinically meaningful classes of the latter scale. Moreover, our feature analysis sheds light on the interrelated nature of the two studied scales: they are both characterized by negative sentiment expressed in text messages and by phone application usage in the morning hours, confirming some previous findings on subjective well-being manifestations. At the same time, SWB measured by Diener’s scale is reflected mostly in lexical features referring to social and affective interactions, while mental well-being is characterized by objective features that reflect physiological functioning, circadian rhythms and somatic conditions, thus saliently demonstrating the underlying theoretical differences between the two scales.

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Predicting Well-being Using Short Ecological Momentary Audio Recordings

Cited by 10 publications

References 31 publications

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Predicting Individual Well-Being in Teamwork Contexts Based on Multi-Modal Speech Features

Predicting subjective well-being in a high-risk sample of Russian mental health app users

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