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

arXiv cs.CL / 4/29/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

Key Points

  • The paper examines whether speech emotion recognition (SER) research’s stated motivations align with the actual datasets and emotions used in commonly studied benchmarks.
  • It finds a recurring mismatch: researchers often aim for deployment-relevant goals like well-situated, voice-activated systems or healthcare use, but prevalent datasets do not represent those target contexts.
  • The authors argue that this motivation-practice gap can create ethical risks, including task validity issues and potential downstream misuse or harms.
  • To address the problem, the paper calls for SER researchers to re-ground their work in concrete, deployment-oriented use cases to avoid misinterpretation and unethical application.

Abstract

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.