Available at: https://digitalcommons.calpoly.edu/theses/1617
Date of Award
MS in Computer Science
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 .” 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.