Multimodal Speaker Identification in Classroom Environments
Michael L. Chrzan, Meghavarshini Krishnaswamy, Robert Gibboni, Katie Wetstone, Wei Ai, Jing Liu
Why It Matters
What makes this one worth your time
Improving speaker identification in educational settings can facilitate automated feedback systems, supporting equitable instruction and personalized learning experiences.
The study enhances speaker identification in classrooms by combining acoustic and semantic data.
Summary
The paper presents a multimodal speaker identification framework for classroom environments, integrating acoustic embeddings with semantic context from language models to improve speaker identification accuracy.
Key contributions
- Development of a multimodal speaker identification framework for classroom environments.
- Integration of acoustic embeddings with LLM-derived semantic context.
- Improved accuracy in distinguishing between teacher and student roles.
Notable insights
- Combining acoustic data with semantic context from language models can significantly improve speaker identification accuracy in noisy environments.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.13712v1 Announce Type: cross Abstract: 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.