A review of depression and suicide risk assessment using speech analysis
By Nicholas Cummins, Stefan Scherer, Jarek Krajewski, Sebastian Schnieder, Julien Epps, Thomas F. Quatieri. Speech Communication, 2015
955 citations recorded by the source corpus.
Subfields: Clinical Psychology, Social Psychology
View Paper: https://www.semanticscholar.org/paper/7d9f6beb6ed00358124c7784d42e94feaae2339b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Clinical Psychology
In Social Psychology
- Maja Pantic
- Björn Schuller
- Mohammad Soleymani
- Michel François Valstar
- Fabien Ringeval
- Jonathan Gratch
- R. Cowie
- Kate H Bentley
Citations in the corpus
- AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019, background): “…human behaviour analysis for computer-aided depression diagnosis based on behavioural cues such as facial expressions and speech prosody, because of convincing evidences that depression and related mental health disorders are associated with changes in patterns of behaviour [8, 11, 28, 58, 78].”
- AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge (2017, methodology): “Further, we provide the scores that every individual provided on the PHQ-8 depression inventory to allow participants to beer model the varied characteristics and symptoms of depression [4].”
- Automated assessment of psychiatric disorders using speech: A systematic review (2019, methodology): “We summarize and discuss different strategies found in the reviewed articles to record speech, avoid confounds, and elicit relevant signals in speech for psychiatric-trait detection while safeguarding privacy (for further discussion, see Reference 99).”
- DepAudioNet: An Efficient Deep Model for Audio based Depression Classification (2016, background): “For a more thorough review on ADD using the audio modality, refer to [10].”