Unrequited Emotions: Investigating the Gaps in Motivation and Practice in Speech Emotion Recognition Research
- URL: http://arxiv.org/abs/2604.25776v1
- Date: Tue, 28 Apr 2026 15:43:35 GMT
- Title: Unrequited Emotions: Investigating the Gaps in Motivation and Practice in Speech Emotion Recognition Research
- Abstract summary: We conduct a systematic survey of speech emotion recognition (SER) research to uncover what stated motivations drive this work.<n>We find that while SER research identifies appealing goals, commonly-used datasets do not reflect these proposed deployment contexts.<n>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.
- Score: 11.170150451606936
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- EGI: A Multimodal Emotional AI Framework for Enhancing Scrum Master Real-time Self-Awareness [0.0]
This paper proposes a novel application integrating four AI models to monitor the unconsciously expressed emotions of Scrum Masters and meeting organizers.<n>The system achieved an ASR word error rate WER of 10% in simulated meeting environments.
arXiv Detail & Related papers (2026-05-17T22:55:20Z) - Ethics of Care for Software Engineering [51.80521257060489]
We argue that the opposition of "caring about" and "caring for" is based on the ethics of care.<n>We advocate for the adoption of ethics of care in software engineering.
arXiv Detail & Related papers (2026-01-23T20:16:00Z) - Emotion Recognition and Generation: A Comprehensive Review of Face, Speech, and Text Modalities [8.08366903467967]
We introduce the fundamental principles underlying emotion recognition and generation across facial, vocal, and textual modalities.<n>We discuss evaluation metrics, comparative analyses, and current limitations, shedding light on the challenges faced by researchers in the field.
arXiv Detail & Related papers (2025-02-02T00:11:19Z) - Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN)
EDEN recognizes emotion and causes in an explicitly thinking way.
It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
arXiv Detail & Related papers (2024-06-07T08:58:29Z) - Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study
on Speech Emotion Recognition [54.952250732643115]
We study Acoustic Word Embeddings (AWEs), a fixed-length feature derived from continuous representations, to explore their advantages in specific tasks.
AWEs have previously shown utility in capturing acoustic discriminability.
Our findings underscore the acoustic context conveyed by AWEs and showcase the highly competitive Speech Emotion Recognition accuracies.
arXiv Detail & Related papers (2024-02-04T21:24:54Z) - CauESC: A Causal Aware Model for Emotional Support Conversation [79.4451588204647]
Existing approaches ignore the emotion causes of the distress.
They focus on the seeker's own mental state rather than the emotional dynamics during interaction between speakers.
We propose a novel framework CauESC, which firstly recognizes the emotion causes of the distress, as well as the emotion effects triggered by the causes.
arXiv Detail & Related papers (2024-01-31T11:30:24Z) - Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
We propose a Dynamic Causal Disentanglement Model based on hidden variable separation.
This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions.
Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
arXiv Detail & Related papers (2023-09-13T12:58:09Z) - Functional Graph Contrastive Learning of Hyperscanning EEG Reveals
Emotional Contagion Evoked by Stereotype-Based Stressors [1.8925617030516924]
This study focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs.
Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects.
arXiv Detail & Related papers (2023-08-22T09:04:14Z) - Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias.
A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
arXiv Detail & Related papers (2021-07-26T10:40:59Z) - Target Guided Emotion Aware Chat Machine [58.8346820846765]
The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions.
This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post.
arXiv Detail & Related papers (2020-11-15T01:55:37Z) - Intrinsic motivation in virtual assistant interaction for fostering
spontaneous interactions [3.420509295457138]
This study aims to cover intrinsic motivation by taking an affective-engineering approach.
A novel motivation model is proposed, in which intrinsic motivation is affected by two factors: expectation of capability and uncertainty.
Results of the first experiment showed that high expectation engenders more intrinsically motivated interaction compared with low expectation.
arXiv Detail & Related papers (2020-10-13T14:23:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.