发表机构
Sorbonne Université; Ircam; CNRS（索邦大学; 声学与音乐研究与协调学院; 法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该博士研究探索非典型听力（如自闭症听觉过敏）下的音频情感识别，通过微调CLAP模型并利用少量标注数据泛化情感反应，以解决评估难题。
AI 中文摘要
我的博士工作旨在探索非典型听力情境下的音频情感识别（AER）。本研究关注自闭症患者的听觉过敏现象，这一现象通常难以评估且因人而异。我们的核心思想是利用从声学特征中理解情感的方法，依赖于从少量标注数据中泛化情感反应的可能性。作为第一步，我们使用低秩适配（LoRA）对大型基础模型对比语言-音频预训练（CLAP）进行微调，该模型在神经典型听众的效价和唤醒度数据集上训练。
英文摘要
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.
Journal refACII : Affective Computing and Intelligent INteraction 2026, Enrique Sucar; Nadia Berthouze; Gary McKeown, Sep 2026, Puebla (Mexico), Mexico
