Turn-Taking Predictions across Languages and Genres Using an LSTM Recurrent Neural Network

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 very good results have recently been obtained [1]. Here we present an improved recurrent network model that outperforms [1] 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.