Emotion Recognition and Depression Diagnosis by Acoustic and Visual Features: A Multimodal Approach
Abstract
There is an enormous number of potential applications of the system which is capable to recognize human emotions. Such opportunity can be useful in various applications, e.g., improvement of Spoken Dialogue Systems (SDSs) or monitoring agents in call-centers. Depression is another aspect of human beings which is closely related to emotions. The system, that can automatically diagnose patient's depression can be helpful to physicians in order to support their decisions and avoid critical mistakes. Therefore, the Affect and Depression Recognition Sub-Challenges (ASC and DSC correspondingly) of the second combined open Audio/Visual Emotion and Depression recognition Challenge (AVEC 2014) is focused on estimating emotions and depression. This study presents the results of multimodal affect and depression recognition based on four different segmentation methods, using support vector regression. Furthermore, a speaker identification procedure has been introduced in order to build the speaker-specific emotion/depression recognition systems.
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Approaches dedicated to depression prediction mainly use direct representations from video without detecting primitive FEs or AUs, based on local, dynamic representations of appearance.
“The approaches dedicated to depression prediction are mainly using direct representations from video without detecting primitive FEs or AUs [253], [254], [255], [256]. They are based on local, dynamic representations of appearance (LBP-TOP or variants) for modelling continuous classification problems.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [254]
For predicting levels of depression, local, dynamic representations of appearance were usually combined with acoustic representations.
“For predicting levels of depression, local, dynamic representations of appearance were usually combined with acoustic representations [253], [254], [255], [256].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [254]
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References 32 · Cited by 56
- Authors
- Sidorov, M., & Minker, W.
- Affiliations
- Ulm University, Ulm, Germany
- Universität Ulm (DE)
- ORCID
- Minker, Wolfgang
- Venue
- Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge
- Publisher
- ACM
- Published
- 2014-11-07
- Pages
- 81-86
- OpenAlex
- W2034327827
- Cited by
- 56
- References
- 32
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 6 · 2022: 3 · 2021: 9 · 2020: 4 · 2019: 5 · 2018: 6 · 2017: 6 · 2016: 3 · 2015: 5
- FWCI
- 7.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Workshop on Audio/Visual Emotion Challenge (AVEC) (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- emotion recognition, depression diagnosis, audio-visual emotion recognition, speech segmentation, support vector regression, speaker identification, spoken dialogue systems
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