Name
Abstract | ||
|---|---|---|
This paper investigates the automatic recognition of social roles that emerge naturally in small groups. These roles represent a flexible classification scheme that can generalize across different scenarios of small group interaction. We systematically investigate various verbal and non-verbal cues extracted from turn-taking patterns, vocal expression, and linguistic style to model speakers behavior. The influence of social roles on the behavior cues exhibited by a speaker is modeled using a discriminative approach based on conditional random fields. Experiments performed on several hours of meeting data reveal that social role recognition using conditional random fields achieves an accuracy of 74% in classifying four social roles and outperforms the baseline method on all social role categories . Furthermore , we also demonstrate the effectiveness of our approach by evaluating it on previously unseen scenarios of small group interactions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TMM.2015.2408437 | Multimedia, IEEE Transactions |
Keywords | Field | DocType |
conditional random fields,crowdsourcing,small group interactions,social roles,support vector machines,speech recognition,psychology,speech,accuracy,hidden markov models,feature extraction | Conditional random field,Computer science,Crowdsourcing,Support vector machine,Classification scheme,Feature extraction,Artificial intelligence,Group interaction,Hidden Markov model,Discriminative model,Machine learning | Journal |
Volume | Issue | ISSN |
17 | 5 | 1520-9210 |
Citations | PageRank | References |
5 | 0.45 | 34 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashtosh Sapru | 1 | 10 | 2.55 |
| Herve Bourlard | 2 | 152 | 37.75 |