mlsanityData to 14 Oct 2026

Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression

arXiv 2306.06760 ↗First posted 11 Jun 2023cs.SD cs.CL eess.AScited by 11

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Abstract (arXiv)

In automatic emotion recognition (AER), labels assigned by different human annotators to the same utterance are often inconsistent due to the inherent complexity of emotion and the subjectivity of perception. Though deterministic labels generated by averaging or voting are often used as the ground truth, it ignores the intrinsic uncertainty revealed by the inconsistent labels. This paper proposes a Bayesian approach, deep evidential emotion regression (DEER), to estimate the uncertainty in emotion attributes. Treating the emotion attribute labels of an utterance as samples drawn from an unknown Gaussian distribution, DEER places an utterance-specific normal-inverse gamma prior over the Gaussian likelihood and predicts its hyper-parameters using a deep neural network model. It enables a joint estimation of emotion attributes along with the aleatoric and epistemic uncertainties. AER experiments on the widely used MSP-Podcast and IEMOCAP datasets showed DEER produced state-of-the-art results for both the mean values and the distribution of emotion attributes.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 23 of its 74 references that are in the map. First posted on arXiv 11 Jun 2023.

Builds on

23 of 74 references.

References · 23

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11 more
  1. 2019SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wildcited by 62
  2. 2018Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modelingcited by 39
  3. 2020Emotion recognition by fusing time synchronous and time asynchronous representationscited by 26
  4. 2018Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselinescited by 23
  5. 2021Representation learning through cross-modal conditional teacher-student training for speech emotion recognitioncited by 17
  6. 2022Probing Speech Emotion Recognition Transformers for Linguistic Knowledgecited by 13
  7. 2020Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognitioncited by 9
  8. 2022Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognitioncited by 8
  9. 2022Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priorscited by 8
  10. 2022Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillationcited by 5
  11. 2022Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVMcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 3; 2026: 1.

11 papers.

  1. 2024Enhancing Zero-shot Text-to-Speech Synthesis with Human Feedbackcited by 20
  2. 2024From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMscited by 13
  3. 2024Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Groundingcited by 10
  4. 2024Bayesian WeakS-to-Strong from Text Classification to Generationcited by 6
  5. 2024The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional Variabilitycited by 3
  6. 2024Conformal Depression Predictioncited by 2
  7. 2025More Similar than Dissimilar: Modeling Annotators for Cross-Corpus Speech Emotion Recognitioncited by 2
  8. 2025ORCA: Open-ended Response Correctness Assessment for Audio Question Answeringcited by 2
  9. 2023It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimationcited by 1
  10. 2025Co-Evidential Fusion with Information Volume for Medical Image Segmentationcited by 0
  11. 2026Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Predictioncited by 0