Common authorship attribution is well described by various authors summed up in Jacques Savoy’s work. Namely, authorship attribution is the process “whereby the author of a given text must be determined based on text samples written by known authors [48].” The field of authorship attribution has been explored in various contexts. Most of these works have been done on the authors written text. This work seeks to approach a similar field to authorship attribution. We seek to attribute not a given author to a work based on style, but a style itself that is used by a group of people. Our work classifies an author into a category based off the spoken dialogue they have said, not text they have written down. Using this system, we differentiate California State Legislators from other entities in a hearing. This is done using audio transcripts of the hearing in question. As this is not Authorship Attribution, the work can better be described as ”Conversational Style Attribution”. Used as a tool in speaker identification classifiers, we were able to increase the accuracy of audio recognition by 50.9%, and facial recognition by 51.6%. These results show that our research into Conversational Style Attribution provides a significant benefit to the speaker identification process.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-2784 |
| Date | 01 June 2016 |
| Creators | Summers, Garrett D |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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