Predicting affect in unconstrained environments remains a fundamental challenge in human-centered AI. While deep neural embeddings dominate contemporary approaches, they often lack interpretability and limit expert-driven refinement. We propose a novel framework that uses Language Models (LMs) as semantic context conditioners over handcrafted affect descriptors to model changes in Valence and Arousal. Our approach begins with interpretable facial geometry and acoustic features derived from structured domain knowledge. These features are transformed into symbolic natural-language descriptions encoding their affective implications. A pretrained LM processes these descriptions to generate semantic context embeddings that act as high-level priors over affective dynamics. Unlike end-to-end black-box pipelines, our framework preserves feature transparency while leveraging the contextual abstraction capabilities of LMs. We evaluate the proposed method on the Aff-Wild2 and SEWA datasets for affect change prediction. Experimental results show consistent improvements in accuracy for both Valence and Arousal compared to handcrafted-only and deep-embedding baselines. Our findings demonstrate that semantic conditioning enables interpretable affect modelling without sacrificing predictive performance, offering a transparent and computationally efficient alternative to fully end-to-end architectures

翻译：摘要：在非受控环境中预测情感仍是人类中心人工智能领域的一项根本性挑战。尽管深度神经嵌入主导了当代方法，但其通常缺乏可解释性并限制了专家驱动的优化。我们提出一种新框架，利用语言模型作为手工情感描述符的语义上下文调节器，对效价和唤醒度的变化进行建模。该方法首先采用基于结构化领域知识导出的可解释面部几何与声学特征，将这些特征转化为编码其情感含义的符号化自然语言描述，并通过预训练语言模型处理描述以生成作为情感动力学高层先验的语义上下文嵌入。与端到端黑箱流水线不同，本框架在保留特征透明性的同时，充分利用语言模型的上下文抽象能力。我们在Aff-Wild2和SEWA数据集上评估了所提方法的情感变化预测性能。实验结果表明，与纯手工特征及深度嵌入基线相比，效价和唤醒度的预测精度均取得持续提升。研究证明，语义条件化能够在不牺牲预测性能的前提下实现可解释的情感建模，为完全端到端架构提供了透明且计算高效的替代方案。