Deep Learning Technique to Diagnose Depression in Audio-Reference-Cited by-同舟云学术

Deep Learning Technique to Diagnose Depression in Audio

Published:2023-08-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing
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Author:

Misra Gargi¹^ORCID,Kumar Divya¹^ORCID

Affiliation:

1. Motilal Nehru National Institute of Technology, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3607947.3608029

Reference24 articles.

1. Tuka Al Hanai Mohammad M Ghassemi and James R Glass. 2018. Detecting Depression with Audio/Text Sequence Modeling of Interviews.. In Interspeech. 1716–1720. Tuka Al Hanai Mohammad M Ghassemi and James R Glass. 2018. Detecting Depression with Audio/Text Sequence Modeling of Interviews.. In Interspeech. 1716–1720.

2. Querying Depression Vlogs

3. Detecting Psychological Distress in Adults Through Transcriptions of Clinical Interviews

4. Analysis of acoustic space variability in speech affected by depression

5. Florian Eyben , Klaus R Scherer , Björn W Schuller , Johan Sundberg , Elisabeth André , Carlos Busso , Laurence Y Devillers , Julien Epps , Petri Laukka , Shrikanth S Narayanan , 2015. The Geneva minimalistic acoustic parameter set (GeMAPS) for voice research and affective computing . IEEE transactions on affective computing 7, 2 ( 2015 ), 190–202. Florian Eyben, Klaus R Scherer, Björn W Schuller, Johan Sundberg, Elisabeth André, Carlos Busso, Laurence Y Devillers, Julien Epps, Petri Laukka, Shrikanth S Narayanan, 2015. The Geneva minimalistic acoustic parameter set (GeMAPS) for voice research and affective computing. IEEE transactions on affective computing 7, 2 (2015), 190–202.