The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Robust Speech and Natural Language Processing Models for Depression Screening
Open accessAbstract
Depression is a global health concern with a critical need for increased patient screening. Speech technology offers advantages for remote screening but must perform robustly across patients. We have described two deep learning models developed for this purpose. One model is based on acoustics; the other is based on natural language processing. Both models employ transfer learning. Data from a depression-labeled corpus in which 11,000 unique users interacted with a human-machine application using conversational speech is used. Results on binary depression classification have shown that both models perform at or above AUC=0.80 on unseen data with no speaker overlap. Performance is further analyzed as a function of test subset characteristics, finding that the models are generally robust over speaker and session variables. We conclude that models based on these approaches offer promise for generalized automated depression screening.
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“addressing the robustness of the model to specific minority groups directly relates to the notion of fairness23–25.”
From A scoping review of robustness concepts for machine learning in healthcare (2025) · cites this paper as [25]
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Sorted by citations
A scoping review of robustness concepts for machine learning in healthcare
Balendran, A., Beji, C., Bouvier, F., Khalifa, O., Evgeniou, T., Ravaud, P., & Porcher, R. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01420-1 · Cited by 43
Metadata
- Authors
- Lu, Y., Harati, A., Rutowski, T., Oliveira, R., Chlebek, P., & Shriberg, E.
- Affiliations
- Ellipsis Health,San Francisco,California,USA
- Ellipsis (US)
- Venue
- 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
- Publisher
- IEEE
- Published
- 2020-12-05
- Pages
- 1-5
- ISSN
- 2372-7241 (ISSN-L)
- OpenAlex
- W3132776082
- Cited by
- 9
- References
- 4
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 2 · 2022: 4
- FWCI
- 1.46
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Signal Processing in Medicine and Biology Symposium (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- depression screening, automated mental health screening, speech analysis, conversational speech, natural language processing, transfer learning
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- The paper
- Lu, Y., Harati, A., Rutowski, T., Oliveira, R., Chlebek, P., & Shriberg, E. (2020). Robust Speech and Natural Language Processing Models for Depression Screening. 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). https://doi.org/10.1109/spmb50085.2020.9353611
