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Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices

The use of voice recordings in both research and industry practice has increased dramatically in recent years—from diagnosing a COVID-19 infection based on patients’ self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in partic...

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Autores principales: Busquet, Francesc, Efthymiou, Fotis, Hildebrand, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228884/
https://www.ncbi.nlm.nih.gov/pubmed/37253958
http://dx.doi.org/10.3758/s13428-023-02139-9
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author Busquet, Francesc
Efthymiou, Fotis
Hildebrand, Christian
author_facet Busquet, Francesc
Efthymiou, Fotis
Hildebrand, Christian
author_sort Busquet, Francesc
collection PubMed
description The use of voice recordings in both research and industry practice has increased dramatically in recent years—from diagnosing a COVID-19 infection based on patients’ self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants’ natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker’s true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to bias correction in the case of distorted speech signals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-023-02139-9.
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spelling pubmed-102288842023-06-01 Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices Busquet, Francesc Efthymiou, Fotis Hildebrand, Christian Behav Res Methods Article The use of voice recordings in both research and industry practice has increased dramatically in recent years—from diagnosing a COVID-19 infection based on patients’ self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants’ natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker’s true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to bias correction in the case of distorted speech signals. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-023-02139-9. Springer US 2023-05-30 /pmc/articles/PMC10228884/ /pubmed/37253958 http://dx.doi.org/10.3758/s13428-023-02139-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Busquet, Francesc
Efthymiou, Fotis
Hildebrand, Christian
Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices
title Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices
title_full Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices
title_fullStr Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices
title_full_unstemmed Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices
title_short Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices
title_sort voice analytics in the wild: validity and predictive accuracy of common audio-recording devices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228884/
https://www.ncbi.nlm.nih.gov/pubmed/37253958
http://dx.doi.org/10.3758/s13428-023-02139-9
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