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Zhang Y, Folarin AA, Dineley J, Conde P, de Angel V, Sun S, Ranjan Y, Rashid Z, Stewart C, Laiou P, Sankesara H, Qian L, Matcham F, White K, Oetzmann C, Lamers F, Siddi S, Simblett S, Schuller BW, Vairavan S, Wykes T, Haro JM, Penninx BWJH, Narayan VA, Hotopf M, Dobson RJB, Cummins N. Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model. J Affect Disord 2024; 355:40-49. [PMID: 38552911 DOI: 10.1016/j.jad.2024.03.106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/01/2024]
Abstract
BACKGROUND Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples. METHODS The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics. RESULTS From the 29 topics identified, we identified 6 risk topics for depression: 'No Expectations', 'Sleep', 'Mental Therapy', 'Haircut', 'Studying', and 'Coursework'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings. LIMITATIONS Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets. CONCLUSION This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.
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Affiliation(s)
- Yuezhou Zhang
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Amos A Folarin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; University College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Health Data Research UK London, University College London, London, UK
| | - Judith Dineley
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; University of Augsburg, Augsburg, Germany
| | - Pauline Conde
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Valeria de Angel
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Shaoxiong Sun
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Yatharth Ranjan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Callum Stewart
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Petroula Laiou
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Heet Sankesara
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Linglong Qian
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Faith Matcham
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; School of Psychology, University of Sussex, Falmer, East Sussex, UK
| | - Katie White
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Carolin Oetzmann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Femke Lamers
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, the Netherlands
| | - Sara Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Björn W Schuller
- University of Augsburg, Augsburg, Germany; GLAM - Group on Language, Audio, & Music, Imperial College London, London, UK
| | | | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Josep Maria Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ InGeest, Amsterdam, the Netherlands
| | | | - Matthew Hotopf
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Richard J B Dobson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; University College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Health Data Research UK London, University College London, London, UK
| | - Nicholas Cummins
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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