Audio emotion recognition for atypical hearing
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Computer Science > Machine Learning
Title:Audio emotion recognition for atypical hearing
Abstract:My doctoral work aims to explore Audio Emotion Recognition (AER) in the context of atypical listening. This research focuses on auditory hypersensitivity in people with autism, a phenomenon that is often difficult to evaluate and unique to each individual. Our core idea is to leverage our understanding of affect from acoustic traits, relying on the possibility of generalizing affective responses from a small amount of annotated data. As a first step, we fine-tune a large foundation model, Contrastive Language-Audio Pretraining (CLAP) using low-rank adaptation (LoRA), trained on a valence and arousal dataset of neurotypical listeners.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.31168 [cs.LG] |
| (or arXiv:2609.31168v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.31168
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | ACII : Affective Computing and Intelligent INteraction 2026, Enrique Sucar; Nadia Berthouze; Gary McKeown, Sep 2026, Puebla (Mexico), Mexico |
Submission history
From: Ulysse roussel [view email] [via CCSD proxy][v1] Fri, 25 Sep 2026 11:59:00 UTC (172 KB)
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