[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124043-en":3,"doc-seo-124043-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124043,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","A Review of Studies Using Machine Learning to Detect Voice Biomarkers for Depression","Voice biomarkers developed using machine learning represent a promising potential biomarker for mental disorders, including depression. The paper provides a narrative review using a systematic search to evaluate how effective voice biomarkers are for identifying depression and to document variations in sample characteristics and research design methods. Nineteen studies (Jan 2019–Feb 2022) were included, with most using classification and reporting metrics such as sensitivity, accuracy, AUC, and F1. Results suggest performance below PHQ-9 benchmarks, with challenges in cross-study comparison due to metric diversity. Recommendations address strengthening generalisability, including testing on unseen data after model development.","A review of studies using machine learning to detect voice biomarkers for depression  \nDonaghy, P. , Ennis, E. , Mulvenna, M. , Bond, RR. , Kennedy, N. , McTear, M. , O'Connell, H. , Blaylock, N. , & Brueckner, R. (2024) . A review of studies using machine learning to detect voice biomarkers for depression. Journal of Technology in Behavioral Science, 1-15 . Advance online publication. [https://doi.org/10.1007/s41347-](https://doi.org/10.1007/s41347-)[ ](https://doi.org/10.1007/s41347-)024-00454-2  \nLink to publication record in Ulster University Research Portal  \nPublished in:  \nJournal of Technology in Behavioral Science  \nPublication Status:  \nPublished online: 12/12/2024  \nDOI:  \n10.1007/s41347-024-00454-2  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nDocument Licence:  \nCC BY  \nGeneral rights  \nThe copyright and moral rights to the output are retained by the output author(s), unless otherwise stated by the document licence.  \nUnless otherwise stated, users are permitted to download a copy of the output for personal study or non-commercial research and are permitted to freely distribute the URL of the output. They are not permitted to alter, reproduce, distribute or make any commercial use of the output without obtaining the permission of the author(s) .  \nIf the document is licenced under Creative Commons, the rights of users of the documents can be found at [https://creativecommons.org/share-your-work/cclicenses/](https://creativecommons.org/share-your-work/cclicenses/) .  \nTake down policy  \nThe Research Portal is Ulster University's institutional repository that provides access to Ulster's research outputs. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. If you discover content in the Research Portal that you believe breaches copyright or violates any law, please contact [pure-support@ulster.ac.uk](pure-support@ulster.ac.uk)  \nDownload date: 15/01/2025  \nA Review of Studies Using Machine Learning to Detect Voice Biomarkers for Depression  \nPhilip Donaghy1 · Edel Ennis2 · Maurice Mulvenna1 · Raymond Bond1 · Niamh Kennedy2 · Mike McTear1 · Henry O’Connell3 · Nate Blaylock3 · Raymond Brueckner3  \nReceived: 7 July 2023 / Revised: 26 June 2024 / Accepted: 28 October 2024 © The Author(s) 2024  \nAbstract  \nVoice biomarkers developed using machine learning are a promising potential biomarker for mental disorders, including depression. This paper presents a narrative review with a systematic search of the evidence surrounding the efficacy of voice biomarkers as indicators of depression. The review considers two research questions: (i) What is the efficacy of voice biomarkers as potential biomarkers for depression? (ii) What are the variations in the samples and design methodologies employed? Nineteen papers were identified as examining voice biomarkers for depression using machine learning methods between January 2019 and February 2022. A subset of guidelines recommended in a previous systematic review was selected and adapted to investigate aspects of the field since that review. Seventeen studies used classification methods, and two used regression methods. Within the papers that examined classification, sensitivity (recall) was used by 76% of papers, accuracy by 65%, AUC by 59%, and F1 score by 59%. From these papers, the average performance achieved for the following metrics was 0.78 for sensitivity (recall), 0.76 for F1 score, and 0.78 for AUC. This review found that the efficacy of vocal biomarkers as indicators for depression is below that of the PHQ-9 form, a tool commonly used in psychology. The PHQ-9 can serve as a benchmark against which to compare these models. Difficulties were observed in comparing these models due to the variety of performance metrics used. Recommendations are presented as to how the generalisability of these models may be strengthened, e.g., testing on unseen data after models are dev","cbCaieo8QYVIzXh2","https://ap.wps.com/l/cbCaieo8QYVIzXh2","pdf",990290,1,16,"English","en",105,"# Abstract\n# Background\n## The Mental Health Crisis\n# Review Methods and Evidence Summary\n## Research questions and included studies\n## Classification vs regression approaches\n# Performance Findings and Benchmarking\n## Reported metric distributions\n## Comparison with PHQ-9\n# Challenges and Recommendations","[{\"question\":\"What is the main purpose of this review on voice biomarkers for depression?\",\"answer\":\"It reviews evidence on how effective voice biomarkers derived from machine learning are for indicating depression, while also describing variations in samples and study design methods.\"},{\"question\":\"Which performance metrics were most commonly used in the included classification studies?\",\"answer\":\"Sensitivity (recall) was used by 76% of papers, with accuracy by 65%, AUC by 59%, and F1 score by 59%. Average reported performance for key metrics was around 0.76–0.78 depending on the measure.\"},{\"question\":\"How does the review compare voice-biomarker models with the PHQ-9 benchmark?\",\"answer\":\"The review finds that vocal biomarkers’ efficacy as indicators for depression is below that of PHQ-9, and PHQ-9 can serve as a benchmark for comparing models.\"},{\"question\":\"What recommendation does the review make to improve the generalisability of these models?\",\"answer\":\"It recommends strengthening generalisability by testing models on unseen data after development, helping ensure performance transfer beyond the training set.\"}]","A Review of Studies Using Machine Learning to Detect Voice Biomarkers for Depression | PDF",1785820070,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"a-review-of-studies-using-machine-learning-to-detect-voice-biomarkers-for-depression","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-review-of-studies-using-machine-learning-to-detect-voice-biomarkers-for-depression/124043/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main purpose of this review on voice biomarkers for depression?","Question",{"text":75,"@type":76},"It reviews evidence on how effective voice biomarkers derived from machine learning are for indicating depression, while also describing variations in samples and study design methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which performance metrics were most commonly used in the included classification studies?",{"text":80,"@type":76},"Sensitivity (recall) was used by 76% of papers, with accuracy by 65%, AUC by 59%, and F1 score by 59%. Average reported performance for key metrics was around 0.76–0.78 depending on the measure.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the review compare voice-biomarker models with the PHQ-9 benchmark?",{"text":84,"@type":76},"The review finds that vocal biomarkers’ efficacy as indicators for depression is below that of PHQ-9, and PHQ-9 can serve as a benchmark for comparing models.",{"name":86,"@type":73,"acceptedAnswer":87},"What recommendation does the review make to improve the generalisability of these models?",{"text":88,"@type":76},"It recommends strengthening generalisability by testing models on unseen data after development, helping ensure performance transfer beyond the training set.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,121,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":120},"healthcare",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},8,"Research & Report",30,"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]