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

arXiv: https://arxiv.org/abs/2604.25776 · HTML: https://mlsanity.com/paper/2604.25776/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 122 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 26 of 26)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Exploration of A Self-Supervised Speech Model: A Study on Emotional Corpora](https://mlsanity.com/paper/2210.02595/) (2022)
- [Responsible AI: Gender bias assessment in emotion recognition](https://mlsanity.com/paper/2103.11436/) (2021)
- [Speech Emotion Recognition Using Multi-hop Attention Mechanism](https://mlsanity.com/paper/1904.10788/) (2019)
- [Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition](https://mlsanity.com/paper/1907.06078/) (2019)
- [Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention](https://mlsanity.com/paper/2106.04133/) (2021)
- [Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling](https://mlsanity.com/paper/2203.08810/) (2022)
- [Speech Emotion Recognition using Semantic Information](https://mlsanity.com/paper/2103.02993/) (2021)
- [DST: Deformable Speech Transformer for Emotion Recognition](https://mlsanity.com/paper/2302.13729/) (2023)
- [Deep Architecture Enhancing Robustness to Noise, Adversarial Attacks, and Cross-corpus Setting for Speech Emotion Recognition](https://mlsanity.com/paper/2005.08453/) (2020)
- [Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?](https://mlsanity.com/paper/2005.01400/) (2020)
- [What Do Machine Learning Researchers Mean by "Reproducible"?](https://mlsanity.com/paper/2412.03854/) (2024)
- [A cross-corpus study on speech emotion recognition](https://mlsanity.com/paper/2207.02104/) (2022)
- [Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models](https://mlsanity.com/paper/2305.19184/) (2023)
- [SpeechEQ: Speech Emotion Recognition based on Multi-scale Unified Datasets and Multitask Learning](https://mlsanity.com/paper/2206.13101/) (2022)
- [What is Learnt by the LEArnable Front-end (LEAF)? Adapting Per-Channel Energy Normalisation (PCEN) to Noisy Conditions](https://mlsanity.com/paper/2404.06702/) (2024)
- [Unsupervised low-rank representations for speech emotion recognition](https://mlsanity.com/paper/2104.07072/) (2021)
- [Integrating Emotion Recognition with Speech Recognition and Speaker Diarisation for Conversations](https://mlsanity.com/paper/2308.07145/) (2023)
- [A Novel Markovian Framework for Integrating Absolute and Relative Ordinal Emotion Information](https://mlsanity.com/paper/2108.04605/) (2021)
- [Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning](https://mlsanity.com/paper/2211.09273/) (2022)
- [ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents](https://mlsanity.com/paper/2005.01777/) (2020)
- [An Attribute-Aligned Strategy for Learning Speech Representation](https://mlsanity.com/paper/2106.02810/) (2021)
- [Once More, With Feeling: Measuring Emotion of Acting Performances in Contemporary American Film](https://mlsanity.com/paper/2411.10018/) (2024)

## 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.