Unrequited Emotions: Investigating the Gaps in Motivation and Practice in Speech Emotion Recognition Research
Taryn Wong, Zeerak Talat, Hanan Aldarmaki, Anjalie Field
TLDR
This paper surveys Speech Emotion Recognition (SER) research, revealing a critical gap between stated motivations and actual research practices, raising ethical concerns.
Key contributions
- Identifies a significant disconnect between stated motivations and actual research practices in Speech Emotion Recognition (SER).
- Systematically surveys SER research to uncover driving motivations and their alignment with datasets and emotions studied.
- Finds that common SER datasets do not reflect proposed deployment contexts like healthcare or voice-activated systems.
- Argues that these gaps create ethical concerns, leading to potential misinterpretation, misuse, and downstream harms.
Why it matters
This paper is crucial for the SER community, highlighting how a misalignment between research goals and methods can lead to ethical issues and real-world harms. It urges researchers to re-evaluate their practices, ensuring their work is grounded in concrete, ethically sound use-cases to prevent misuse and misinterpretation.
Original 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.
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