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Presentation #3
Session:Dialogue
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; University of Texas at El Paso 
 Diego Aguirre; University of Texas at El Paso 
 Gerardo Cervantes; University of Texas at El Paso 
 Olac Fuentes; University of Texas at El Paso 
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.