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Generalization of Deep Acoustic and NLP Models for Large-Scale Depression Screening

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Biomedical Sensing and Analysis

Abstract

Depression is a costly and underdiagnosed global health concern, and there is a great need for improved patient screening. Speech technology offers promise for remote screening, but must perform robustly across patient and environmental variables. This chapter describes two deep learning models that achieve excellent performance in this regard. An acoustic model uses transfer learning from an automatic speech recognition (ASR) task. A natural language processing (NLP) model uses transfer learning from a language modeling task. Both models are studied using data from over 10,000 unique users who interacted with human-machine applications using conversational speech. Results for binary classification on a large test set show AUC performance of 0.79 and 0.83 for the acoustic and NLP models, respectively. RMSE for a regression task is 4.70 for the acoustic model and 4.27 for the NLP model. Further analysis of performance as a function of test subset characteristics indicates that the models are generally robust over speaker and session variables. It is concluded that both acoustic and NLP-based models have potential for use in generalized automated depression screening.

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Acknowledgments

This paper and the research behind it would not have been possible without the exceptional support of the Ellipsis Health team. We specially thank our clinical team: Mike Aratow, Farshid Haque, Tahmida Nazreen and Melissa McCool. We thank Mainul Mondal and Susan Solinsky for their continuing support. Additionally, we thank Vanessa Lin and Nina Roth for editing the manuscript for this chapter.

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Authors and Affiliations

  1. Ellipsis Health Inc., San Francisco, CA, USA

    Amir Harati, Tomasz Rutowski, Yang Lu, Piotr Chlebek, Ricardo Oliveira, Elizabeth Shriberg & David Lin

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  1. Amir Harati
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  2. Tomasz Rutowski
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  3. Yang Lu
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  4. Piotr Chlebek
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  5. Ricardo Oliveira
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  6. Elizabeth Shriberg
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  7. David Lin
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Correspondence to Amir Harati .

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Editors and Affiliations

  1. Department of Electrical & Computer Engineering, Temple University, Philadelphia, PA, USA

    Iyad Obeid

  2. Department of Electrical & Computer Engineering, Temple University, Philadelphia, PA, USA

    Joseph Picone

  3. Tandon School of Engineering, New York University, Brooklyn, NY, USA

    Ivan Selesnick

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Harati, A. et al. (2022). Generalization of Deep Acoustic and NLP Models for Large-Scale Depression Screening. In: Obeid, I., Picone, J., Selesnick, I. (eds) Biomedical Sensing and Analysis . Springer, Cham. https://doi.org/10.1007/978-3-030-99383-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-99383-2_3

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