Using Neural Networks for Data-Driven Backchannel Prediction: A Survey on Input Features and Training Techniques

In order to make human computer interaction more social, the use of supporting backchannel cues can be beneficial. Such cues can be delivered in different channels like vision, speech or gestures. In this work, we focus on the prediction of acoustic backchannels in terms of speech. Previously, this prediction has been accomplished by using rule-based approaches. But like every rule-based implementation, it is dependent on a fixed set of handwritten rules which have to be changed every time the mechanism is adjusted or different data is used. In this paper we want to overcome these limitations by making use of recent advancements in the field of machine learning. We show that backchannel predictions can be generated by means of a neural network based approach. Such a method has the advantage of depending only on the training data, without the need of handwritten rules.