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Audio emotion recognition for atypical hearing

Sep 2026 · 2026 14th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · pp. 1-5 · 0 citations · 42 references
Computer Science

TL;DR

This research focuses on auditory hypersensitivity in people with autism, a phenomenon that is often difficult to evaluate and unique to each individual.

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.

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