Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition

[2406.05065] Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Audio and Speech Processing arXiv:2406.05065 (eess) [Submitted on 7 Jun 2024 ( v1 ), last revised 5 Sep 2024 (this version, v2)] Title: Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition Authors: Yi-Cheng Lin , Haibin Wu , Huang-Cheng Chou , Chi-Chun Lee , Hung-yi Lee View a PDF of the paper titled Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition, by Yi-Cheng Lin and 4 other authors View PDF HTML (experimental) Abstract: The rapid growth of Speech Emotion Recognition (SER) has diverse global applications, from improving human-computer interactions to aiding mental health diagnostics. However, SER models might contain social bias toward gender, leading to unfair outcomes. This study analyzes gender bias in SER models trained with Self-Supervised Learning (SSL) at scale, exploring factors influencing it. SSL-based SER models are chosen for their cutting-edge performance. Our research pioneering research gender bias in SER from both upstream model and data perspectives. Our findings reveal that females exhibit slightly higher overall SER performance than males. Modified CPC and XLS-R, two well-known SSL models, notably exhibit significant bias. Moreover, models trained with Mandarin datasets display a pronounced bias toward valence. Lastly, we find that gender-wise emotion distribution differences in training data significantly affect gender bias, while upstream model representation has a limited impact. Comments: Accepted by INTERSPEECH 2024 Subjects: Audio and Speech Processing (eess.AS) Cite as: arXiv:2406.05065 [eess.AS] (or arXiv:2406.05065v2 [eess.AS] for this version) https://doi.org/10.48550/arXiv.2406.05065 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proc. Interspeech 2024, 4633-4637 Related DOI : https://doi.org/10.21437/Interspeech.2024-1073 Focus to learn more DOI(s) linking to related resources Submission history From: Yi-Cheng Lin [ view email ] [v1] Fri, 7 Jun 2024 16:36:50 UTC (391 KB) [v2] Thu, 5 Sep 2024 16:46:37 UTC (391 KB) Full-text links: Access Paper: View a PDF of the paper titled Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition, by Yi-Cheng Lin and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.AS < prev | next > new | recent | 2024-06 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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