| Presentation # | 3 |
| Session: | Dialogue |
| Location: | Kallirhoe Hall |
| Session Time: | Thursday, December 20, 10:00 - 12:00 |
| Presentation Time: | Thursday, December 20, 10:00 - 12:00 |
| Presentation: |
Poster
|
| Topic: |
Spoken dialog systems: |
| Paper Title: |
TURN-TAKING PREDICTIONS ACROSS LANGUAGES AND GENRES USING AN LSTM RECURRENT NEURAL NETWORK |
| Authors: |
Nigel Ward, Diego Aguirre, Gerardo Cervantes, Olac Fuentes, University of Texas at El Paso, United States |
| Abstract: |
Going beyond turn-taking models built to solve specific tasks, such as predicting if a user will hold his/her turn after a pause, there is growing interest in more general models for turn taking that subsume many such tasks, and good results have recently been obtained (Skantze 2017). Here we present a recurrent network model that outperforms (Skanze 2017) and does so without requiring lexical annotation. Further, we show that this model can be trained for different languages with no modifications, providing good results in turn-taking prediction for English, Spanish, Japanese, Mandarin and French. We also show that our model performs well across genres, including task-oriented dialog and general conversation. |