  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
