research-article

Detecting Depression in Less Than 10 Seconds: Impact of Speaking Time on Depression Detection Sensitivity

Authors:

Nujud Aloshban,

Anna Esposito,

Alessandro VinciarelliAuthors Info & Claims

ICMI '20: Proceedings of the 2020 International Conference on Multimodal Interaction

Pages 79 - 87

https://doi.org/10.1145/3382507.3418875

Published: 22 October 2020 Publication History

Get Access

Abstract

This article investigates whether it is possible to detect depression using less than 10 seconds of speech. The experiments have involved 59 participants (including 29 that have been diagnosed with depression by a professional psychiatrist) and are based on a multimodal approach that jointly models linguistic (what people say) and acoustic (how people say it) aspects of speech using four different strategies for the fusion of multiple data streams. On average, every interview has lasted for 242.2 seconds, but the results show that 10 seconds or less are sufficient to achieve the same level of recall (roughly 70%) observed after using the entire inteview of every participant. In other words, it is possible to maintain the same level of sensitivity (the name of recall in clinical settings) while reducing by 95%, on average, the amount of time requireed to collect the necessary data.

Supplementary Material

MP4 File (3382507.3418875.mp4)

This article investigates whether it is possible to detect depression using less than 10 seconds of speech. The experiments have involved 59 participants (including 29 that have been diagnosed with depression by a professional psychiatrist) and are based on a multimodal approach that jointly models linguistic (what people say) and acoustic (how people say it) aspects of speech using four different strategies for the\r\nfusion of multiple data streams. On average, every interview has lasted for 242.2 seconds, but the results show that 10 seconds or less are sufficient to achieve the same level of recall (roughly 70%) observed after using the entire interview of every participant. In other words, it is possible to maintain the same level of sensitivity (the name of recall in clinical settings) while reducing by 95%, on average, the amount of\r\ntime required to collect the necessary data.

Download
11.50 MB

References

[1]

T. Al Hanai, M.M. Ghassemi, and J.R. Glass. 2018. Detecting Depression with Audio/Text Sequence Modeling of Interviews. In Proceedings of Interspeech. 1716--1720.

Abstract

Supplementary Material

References

Cited By

Index Terms

Recommendations

Detecting Depression Severity from Vocal Prosody

Avoiding dominance of speaker features in speech-based depression detection

A Paralinguistic Approach To Speaker Diarisation: Using Age, Gender, Voice Likability and Personality Traits

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations