RT Journal Article
T1 A data-driven approach to spoken dialog segmentation
A1 Griol Barres, David
A1 Molina López, José Manuel
A1 Sanchis de Miguel, María Araceli
A1 Callejas Carrión, Zoraida
AB In This Paper, We Present A Statistical Model For Spoken Dialog Segmentation That Decides The Current Phase Of The Dialog By Means Of An Automatic Classification Process. We Have Applied Our Proposal To Three Practical Conversational Systems Acting In Different Domains. The Results Of The Evaluation Show That Is Possible To Attain High Accuracy Rates In Dialog Segmentation When Using Different Sources Of Information To Represent The User Input. Our Results Indicate How The Module Proposed Can Also Improve Dialog Management By Selecting Better System Answers. The Statistical Model Developed With Human-Machine Dialog Corpora Has Been Applied In One Of Our Experiments To Human-Human Conversations And Provides A Good Baseline As Well As Insights In The Model Limitation.
PB Elsevier
SN 0925-2312
YR 2020
FD 2020-05-28
LK https://hdl.handle.net/10016/33543
UL https://hdl.handle.net/10016/33543
LA eng
DS e-Archivo
RD 1 sept. 2024