Automated analysis of K-12 classroom dynamics faces challenges due to background noise and variable child speech, often confounding acoustic-only models. This study evaluates a multimodal speaker identification framework anchoring acoustic embeddings with LLM-derived semantic context. Using a subset of the EDSI dataset (8 math classrooms, N = 2,801 utterances), we found an acoustic baseline (ECAPA-TDNN) achieved only 39.0% accuracy. By integrating transcript-based "contextual anchoring" into a gradient boosting classifier, our multimodal approach raised student identification to 50.3%. Performance also improved for utterances over 5 seconds, reaching 76.9% accuracy (vs. 64.9% baseline) with a 90.9% Top-3 accuracy. Additionally, the model distinguished teacher vs. student roles with 99.3% accuracy. This approach advances the feasibility of automated feedback systems capable of considering individual student participation, a crucial step for supporting equitable instruction at scale.

翻译：K-12课堂动态的自动化分析面临背景噪声和儿童语音多变性的挑战，这常常使纯声学模型性能受限。本研究评估了一种将声学嵌入与基于大语言模型（LLM）的语义上下文相结合的多模态说话人识别框架。利用EDSI数据集子集（8个数学课堂，N=2801条语句），我们发现声学基线模型（ECAPA-TDNN）仅达到39.0%的准确率。通过将基于转录文本的“上下文锚定”融入梯度提升分类器，我们的多模态方法将学生识别准确率提升至50.3%。对于时长超过5秒的语句，性能进一步改善：准确率达76.9%（基线为64.9%），Top-3准确率达90.9%。此外，该模型区分教师与学生角色的准确率达99.3%。该方法推动了自动化反馈系统的可行性发展，使其能够考虑个体学生的参与情况，这是支持大规模公平教学的关键步骤。