mlsanityData to 7 Oct 2026

Are you sure? Analysing Uncertainty Quantification Approaches for Real-world Speech Emotion Recognition

arXiv 2407.01143 ↗First posted 1 Jul 2024cs.SD cs.AI eess.AScited by 4

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Abstract (arXiv)

Uncertainty Quantification (UQ) is an important building block for the reliable use of neural networks in real-world scenarios, as it can be a useful tool in identifying faulty predictions. Speech emotion recognition (SER) models can suffer from particularly many sources of uncertainty, such as the ambiguity of emotions, Out-of-Distribution (OOD) data or, in general, poor recording conditions. Reliable UQ methods are thus of particular interest as in many SER applications no prediction is better than a faulty prediction. While the effects of label ambiguity on uncertainty are well documented in the literature, we focus our work on an evaluation of UQ methods for SER under common challenges in real-world application, such as corrupted signals, and the absence of speech. We show that simple UQ methods can already give an indication of the uncertainty of a prediction and that training with additional OOD data can greatly improve the identification of such signals.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

19 of 33 references.

References · 19

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  4. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 813
  5. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  6. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  7. 2019PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognitioncited by 563
  8. 2018Predictive Uncertainty Estimation via Prior Networkscited by 555
  9. 2015MUSAN: A Music, Speech, and Noise Corpuscited by 400
  10. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  11. 2019Deep Evidential Regressioncited by 354
  12. 2022Dawn of the transformer era in speech emotion recognition: closing the valence gapcited by 155
7 more
  1. 2021Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Trainingcited by 148
  2. 2020Uncertainty-Aware Deep Classifiers using Generative Modelscited by 41
  3. 2022CochlScene: Acquisition of acoustic scene data using crowdsourcingcited by 33
  4. 2023Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblescited by 30
  5. 2021EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognitioncited by 17
  6. 2022Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priorscited by 8
  7. 2019Multimodal Speech Emotion Recognition and Ambiguity Resolutioncited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Uncertainty Modeling in Multimodal Speech Analysis Across the Psychosis Spectrumcited by 1
  2. 2025Performance of Conformal Prediction in Capturing Aleatoric Uncertaintycited by 1
  3. 2026U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable explorationcited by 0
  4. 2026When Does Quality-Aware Multimodal Fusion Matter? A Leakage-Safe Diagnostic for Decision-Level Dependencecited by 0