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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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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. |
format | Online Article Text |
id | pubmed-10228884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
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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