Remote smartphone-based speech collection: acceptance and barriers in individuals with major depressive disorder
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Abstract (arXiv)
The ease of in-the-wild speech recording using smartphones has sparked considerable interest in the combined application of speech, remote measurement technology (RMT) and advanced analytics as a research and healthcare tool. For this to be realised, the acceptability of remote speech collection to the user must be established, in addition to feasibility from an analytical perspective. To understand the acceptance, facilitators, and barriers of smartphone-based speech recording, we invited 384 individuals with major depressive disorder (MDD) from the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) research programme in Spain and the UK to complete a survey on their experiences recording their speech. In this analysis, we demonstrate that study participants were more comfortable completing a scripted speech task than a free speech task. For both speech tasks, we found depression severity and country to be significant predictors of comfort. Not seeing smartphone notifications of the scheduled speech tasks, low mood and forgetfulness were the most commonly reported obstacles to providing speech recordings.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Apr 2021.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.
- 2023Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic modelcited by 2
- 2024Toward Corpus Size Requirements for Training and Evaluating Depression Risk Models Using Spoken Languagecited by 1
- 2026Beyond .WAV: Design and Software Verification of VocalCap, a Traceable Browser-Based Audio Capture System for Vocal Biomarker Researchcited by 0
- 2026Beyond .WAV: Design and Software Verification of VocalCap, a Traceable Browser-Based Audio Capture System for Vocal Biomarker Research3 Sep 2026
- 2024Toward Corpus Size Requirements for Training and Evaluating Depression Risk Models Using Spoken Language31 Dec 2024
- 2023Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model22 Aug 2023