Depression Detection from Short Utterances via Diverse Smartphones in Natural Environmental Conditions
Open accessAbstract
Depression is a leading cause of disease burden worldwide, however there is an unmet need for screening and diagnostic measures that can be widely deployed in real-world environments. Voice-based diagnostic methods are convenient, non-invasive to elicit, and can be collected and processed in near real-time using modern smartphones, smart speakers, and other devices. Studies in voice-based depression detection to date have primarily focused on laboratory-collected voice samples, which are not representative of typical user environments or devices. This paper conducts the first investigation of voice-based depression assessment techniques on real-world data from 887 speakers, recorded using a variety of different smartphones. Evaluations on 16 hours of speech show that conservative segment selection strategies using highly thresholded voice activity detection, coupled with tailored normalization approaches are effective for mitigating smartphone channel variability and background environmental noise. Together, these strategies can achieve F1 scores comparable with or better than those from a combination of clean recordings, a single recording environment and long utterances. The scalability of speech elicitation via smartphone allows detailed models dependent on gender, smartphone manufacturer and/or elicitation task. Interestingly, results herein suggest that normalization based on these criteria may be more effective than tailored models for detecting depressed speech.
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Multiple studies have demonstrated the effectiveness of AI-enabled emotion analysis in assessing patients' depressive states and recommending timely intervention, thereby improving mental health care.
“Multiple studies have demonstrated the effectiveness of AI-enabled emotion analysis in assessing patients’ depressive states and recommending timely intervention, thereby improving mental health care [22,26].”
From Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study (2023) · cites this paper as [22]
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- Authors
- Huang, Z., Epps, J., Joachim, D., & Chen, M.
- Affiliations
- UNSW Sydney (AU)
- Venue
- Interspeech 2018
- Publisher
- ISCA
- Published
- 2018-09-02
- Pages
- 3393-3397
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2888872770
- Cited by
- 52
- References
- 33
- Citations by year
- 2025: 5 · 2024: 12 · 2023: 9 · 2022: 11 · 2021: 6 · 2020: 5 · 2019: 4
- FWCI
- 4.57
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- Locations
- Interspeech (USB) (publishedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- Funders
- Commonwealth Scientific and Industrial Research Organisation (DATA61)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- depression detection, voice-based diagnosis, short utterances, voice activity detection, speech normalization, environmental noise, channel variation, speech elicitation, gender effects
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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