Using Speech Technology for Quantifying Behavioral Characteristics in Peer-Led Team Learning Sessions
This work addresses the need for automated behavioral analysis in educational settings like PLTL, though it is incremental as it builds on existing speech processing methods.
The paper tackled the problem of analyzing behavioral characteristics in Peer-Led Team Learning sessions by developing a speech system that uses robust speech activity detection and speaker diarization to extract features like participation and engagement, with results suggesting applicability to small-group conversations.
Peer-Led Team Learning (PLTL) is a learning methodology where a peer-leader co-ordinate a small-group of students to collaboratively solve technical problems. PLTL have been adopted for various science, engineering, technology and maths courses in several US universities. This paper proposed and evaluated a speech system for behavioral analysis of PLTL groups. It could help in identifying the best practices for PLTL. The CRSS-PLTL corpus was used for evaluation of developed algorithms. In this paper, we developed a robust speech activity detection (SAD) by fusing the outputs of a DNN-based pitch extractor and an unsupervised SAD based on voicing measures. Robust speaker diarization system consisted of bottleneck features (from stacked autoencoder) and informed HMM-based joint segmentation and clustering system. Behavioral characteristics such as participation, dominance, emphasis, curiosity and engagement were extracted by acoustic analyses of speech segments belonging to all students. We proposed a novel method for detecting question inflection and performed equal error rate analysis on PLTL corpus. In addition, a robust approach for detecting emphasized speech regions was also proposed. Further, we performed exploratory data analysis for understanding the distortion present in CRSS-PLTL corpus as it was collected in naturalistic scenario. The ground-truth Likert scale ratings were used for capturing the team dynamics in terms of student's responses to a variety of evaluation questions. Results suggested the applicability of proposed system for behavioral analysis of small-group conversations such as PLTL, work-place meetings etc.. Keywords- Behavioral Speech Processing, Bottleneck Features, Curiosity, Deep Neural Network, Dominance, Auto-encoder, Emphasis, Engagement, Peer-Led Team Learning, Speaker Diarization, Small-group Conversations