# Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression

arXiv: https://arxiv.org/abs/2306.06760 · HTML: https://mlsanity.com/paper/2306.06760/

## 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 (most cited first, 23 of 23)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild](https://mlsanity.com/paper/1901.02839/) (2019)
- [Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling](https://mlsanity.com/paper/1806.06228/) (2018)
- [Emotion recognition by fusing time synchronous and time asynchronous representations](https://mlsanity.com/paper/2010.14102/) (2020)
- [Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines](https://mlsanity.com/paper/1803.07427/) (2018)
- [Representation learning through cross-modal conditional teacher-student training for speech emotion recognition](https://mlsanity.com/paper/2112.00158/) (2021)
- [Probing Speech Emotion Recognition Transformers for Linguistic Knowledge](https://mlsanity.com/paper/2204.00400/) (2022)
- [Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognition](https://mlsanity.com/paper/2002.11312/) (2020)
- [Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition](https://mlsanity.com/paper/2201.11826/) (2022)
- [Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors](https://mlsanity.com/paper/2203.04443/) (2022)
- [Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation](https://mlsanity.com/paper/2207.03334/) (2022)
- [Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVM](https://mlsanity.com/paper/2210.14495/) (2022)

## Built on by (most cited first, 11 of 11)

- [Enhancing Zero-shot Text-to-Speech Synthesis with Human Feedback](https://mlsanity.com/paper/2406.00654/) (2024)
- [From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs](https://mlsanity.com/paper/2408.17026/) (2024)
- [Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding](https://mlsanity.com/paper/2408.16272/) (2024)
- [Bayesian WeakS-to-Strong from Text Classification to Generation](https://mlsanity.com/paper/2406.03199/) (2024)
- [The Whole Is Bigger Than the Sum of Its Parts: Modeling Individual Annotators to Capture Emotional Variability](https://mlsanity.com/paper/2408.11956/) (2024)
- [Conformal Depression Prediction](https://mlsanity.com/paper/2405.18723/) (2024)
- [More Similar than Dissimilar: Modeling Annotators for Cross-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2509.12295/) (2025)
- [ORCA: Open-ended Response Correctness Assessment for Audio Question Answering](https://mlsanity.com/paper/2512.09066/) (2025)
- [It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation](https://mlsanity.com/paper/2310.00486/) (2023)
- [Co-Evidential Fusion with Information Volume for Medical Image Segmentation](https://mlsanity.com/paper/2506.02492/) (2025)
- [Beyond the Mean: Modelling Annotation Distributions in Continuous Affect Prediction](https://mlsanity.com/paper/2604.07198/) (2026)

## 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.