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Unrequited Emotions: Investigating the Gaps in Motivation and Practice in Speech Emotion Recognition Research

arXiv 2604.25776 ↗First posted 28 Apr 2026cs.CLcited by 0

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Abstract (arXiv)

Critical analyses of emotion recognition technology have raised ethical concerns around task validity and potential downstream impacts, urging researchers to ensure alignment between their stated motivations and practice. However, these discussions have not adequately influenced or drawn from research on speech emotion recognition (SER). We address this gap by conducting a systematic survey of SER research to uncover what stated motivations drive this work and if they align with the datasets and emotions studied. We find that while SER research identifies appealing goals, such as well-situated voice-activated systems or healthcare applications, commonly-used datasets do not reflect these proposed deployment contexts, thus presenting a gap between motivations and research practices. We argue that such gaps engender ethical concerns, and that SER research should reassert itself with concrete use-cases to prevent misinterpretations, misuse, and downstream harms.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 122 references that are in the map. First posted on arXiv 28 Apr 2026.

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26 of 122 references.

References · 26

  1. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  2. 2019Measurement and Fairnesscited by 299
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  5. 2022Exploration of A Self-Supervised Speech Model: A Study on Emotional Corporacited by 31
  6. 2021Responsible AI: Gender bias assessment in emotion recognitioncited by 22
  7. 2019Speech Emotion Recognition Using Multi-hop Attention Mechanismcited by 18
  8. 2019Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognitioncited by 15
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  10. 2022Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labelingcited by 12
  11. 2021Speech Emotion Recognition using Semantic Informationcited by 11
  12. 2023DST: Deformable Speech Transformer for Emotion Recognitioncited by 11
14 more
  1. 2020Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognitioncited by 10
  2. 2020Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?cited by 9
  3. 2024What Do Machine Learning Researchers Mean by "Reproducible"?cited by 8
  4. 2022A cross-corpus study on speech emotion recognitioncited by 7
  5. 2023Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Modelscited by 6
  6. 2024What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditionscited by 4
  7. 2021Unsupervised low-rank representations for speech emotion recognitioncited by 3
  8. 2022SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learningcited by 3
  9. 2023Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversationscited by 3
  10. 2021A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Informationcited by 2
  11. 2022Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learningcited by 2
  12. 2020ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agentscited by 1
  13. 2021An Attribute-Aligned Strategy for Learning Speech Representationcited by 1
  14. 2024Once More, With Feeling: Measuring Emotion of Acting Performances in Contemporary American Filmcited by 1

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