  Classroom activity detection (CAD) focuses on accurately classifying whether
the teacher or student is speaking and recording both the length of individual
utterances during a class. A CAD solution helps teachers get instant feedback
on their pedagogical instructions. This greatly improves educators' teaching
skills and hence leads to students' achievement. However, CAD is very
challenging because (1) the CAD model needs to be generalized well enough for
different teachers and students; (2) data from both vocal and language
modalities has to be wisely fused so that they can be complementary; and (3)
the solution shouldn't heavily rely on additional recording device. In this
paper, we address the above challenges by using a novel attention based neural
framework. Our framework not only extracts both speech and language
information, but utilizes attention mechanism to capture long-term semantic
dependence. Our framework is device-free and is able to take any classroom
recording as input. The proposed CAD learning framework is evaluated in two
real-world education applications. The experimental results demonstrate the
benefits of our approach on learning attention based neural network from
classroom data with different modalities, and show our approach is able to
outperform state-of-the-art baselines in terms of various evaluation metrics.

Audio and Speech Processing;Machine Learning;Sound