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Deep learning for robust feature generation in audiovisual emotion recognition
Abstract
Automatic emotion recognition systems predict high-level affective content from low-level human-centered signal cues. These systems have seen great improvements in classification accuracy, due in part to advances in feature selection methods. However, many of these feature selection methods capture only linear relationships between features or alternatively require the use of labeled data. In this paper we focus on deep learning techniques, which can overcome these limitations by explicitly capturing complex non-linear feature interactions in multimodal data. We propose and evaluate a suite of Deep Belief Network models, and demonstrate that these models show improvement in emotion classification performance over baselines that do not employ deep learning. This suggests that the learned high-order non-linear relationships are effective for emotion recognition.
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“Kim et al. [7] developed a suite of deep belief net- work models that showed improvements in emotion clas- sification performance over baselines that do not use deep learning.”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [7]
In the citing paper
“Kim et al. [159] and others (e.g., [149], [148]) demonstrated how this can be achieved with DBNs.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [159]
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Metadata
- Authors
- Kim, Y., Lee, H., & Provost, E. M.
- Affiliations
- University of Michigan (US)
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 3687-3691
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2009059481
- Cited by
- 401
- References
- 39
- Citations by year
- 2026: 5 · 2025: 19 · 2024: 22 · 2023: 18 · 2022: 33 · 2021: 36 · 2020: 44 · 2019: 50 · 2018: 49 · 2017: 47 · 2016: 29 · 2015: 30 · 2014: 17 · 2013: 2
- FWCI
- 35.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- audio-visual emotion recognition, deep belief network, feature selection, multimodal data, emotion classification, higher-order interactions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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