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Automatic Recognition of Personality Traits: A Multimodal Approach
| Content Provider | ACM Digital Library |
|---|---|
| Author | Sidorov, Maxim Ultes, Stefan Schmitt, Alexander |
| Abstract | A system being capable of recognize personality traits may be utilized in an enormous number of applications. Adding personality-dependency may be useful to build speaker-adaptive models, e.g., to improve Spoken Dialogue Systems (SDSs) or to monitor agents in call-centers. Therefore, the First Audio/Visual Mapping Personality Traits Challenge (MAPTRAITS 2014) focuses on estimating personality traits. In this context, this study presents the results for multimodal recognition of personality traits using support vector machines. As only small portions of the data is used for personality estimation at a time (which are later combined to a final estimate), different segmentation methods (and how to derive a final hypothesis) are analyzed regarding the task as both a regression and a classification problem. |
| Starting Page | 11 |
| Ending Page | 15 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450339568 |
| DOI | 10.1145/2668024.2668028 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Feature-based fusion Audio-visual features Personality traits Support vector machine |
| Content Type | Text |
| Resource Type | Article |
