000007640 001__ 7640
000007640 005__ 20231129124936.0
000007640 0247_ $$2DOI$$a10.6083/m4j965jd
000007640 037__ $$aETD
000007640 245__ $$aUsing past speaker behavior to better predict turn transitions
000007640 260__ $$bOregon Health and Science University
000007640 269__ $$a2017
000007640 336__ $$aThesis
000007640 502__ $$bM.S.
000007640 520__ $$aConversations are at the core of everyday social interactions. The interactions between conversants are preformed within the realm of a sophisticated and self-managed turn taking system. In human conversations, the turn taking system supports minimal speaker overlap during turn transitions and minimum gaps between turns. Spoken dialogue systems are a new form of conversational user interface that permits users to use their voice to interact with the computer. As such, the turn taking capabilities of SDS should evolve from a simple timeout to a more human-like model. Recent advances in turn taking systems for SDS use different local features of the last few utterances to predict turn transition. This thesis explores using a summary of past speaker behavior to better predict turn transitions. We believe that the summary features represent an evolving model of the other conversant.
000007640 650__ $$aHumans $$020376
000007640 650__ $$aVoice$$027899
000007640 650__ $$aSpeech $$026226
000007640 650__ $$aInterpersonal Relations $$020939
000007640 650__ $$aComputers $$016992
000007640 650__ $$aCommunication $$016935
000007640 691__ $$aSchool of Medicine$$041369
000007640 7001_ $$aMeshorer, Tomer M.S.
000007640 8564_ $$9488885ec-8050-442a-a9ec-d393dff47b3e$$s572568$$uhttps://digitalcollections.ohsu.edu/record/7640/files/Meshorer.Tomer.2017.pdf
000007640 905__ $$a/rest/prod/bn/99/97/45/bn9997455
000007640 909CO $$ooai:digitalcollections.ohsu.edu:7640$$pstudent-work
000007640 980__ $$aTheses and Dissertations