Towards the automated social analysis of situated speech data

Authors:
Danny Wyatt;Jeff Bilmes;Tanzeem Choudhury;James A. Kitts
Affiliations:
University of Washington;University of Washington;Dartmouth College;Columbia University
Venue:
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Year:
2008

Citing 5
Cited 6

Predicting human interruptibility with sensors

ACM Transactions on Computer-Human Interaction (TOCHI)
The Mobile Sensing Platform: An Embedded Activity Recognition System

IEEE Pervasive Computing
Context-Aware Computing Applications

WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
Discrete-time speech signal processing: principles and practice

Discrete-time speech signal processing: principles and practice
Learning hidden curved exponential family models to infer face-to-face interaction networks from situated speech data

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2

Neary: conversation field detection based on similarity of auditory situation

Proceedings of the 10th workshop on Mobile Computing Systems and Applications
Bridging the gap between physical location and online social networks

Proceedings of the 12th ACM international conference on Ubiquitous computing
EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

Proceedings of the 12th ACM international conference on Ubiquitous computing
Chameleons in imagined conversations: a new approach to understanding coordination of linguistic style in dialogs

CMCL '11 Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
Passive and In-Situ assessment of mental and physical well-being using mobile sensors

Proceedings of the 13th international conference on Ubiquitous computing
NLify: lightweight spoken natural language interfaces via exhaustive paraphrasing

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an automated approach for studying fine-grained details of social interaction and relationships. Specifically, we analyze the conversational characteristics of a group of 24 individuals over a six-month period, explore the relationship between conversational dynamics and network position, and identify behavioral correlates of tie strengths within a network. The ability to study conversational dynamics and social networks over long time scales, and to investigate their interplay with rigor, objectivity, and transparency will complement the traditional methods for scientific inquiry into social dynamics. They may also enable socially aware ubiquitous computing systems that are cognizant of and responsive to the user's engagement with her social environment.