Using these predictions we are able to anticipate utterance endpoints, and start our turns accordingly. LSTM-based turn-taking models that operate in a similar predictive fashion were proposed by <cite>Skantze</cite> in <cite>[13]</cite> . In <cite>these models</cite> LSTMs are used to make continuous predictions of a person's speech activity at each individual time step of 50ms. <cite>The networks</cite> are trained to predict a vector of probability scores for speech activity in each individual frame within a set future window. Rather than designing classifiers to make specific decisions, <cite>these continuous models</cite> are able to capture general information about turn-taking in the data that <cite>they</cite> are trained on. <cite>They</cite> can therefore be applied to a wide variety of turn-taking prediction tasks and have been shown to outperform traditional classifiers when applied to HOLD/SHIFT predictions. A downside to the approach in <cite>[13]</cite> is that, since a single LSTM is being used, all input features must be processed at the same rate.