Prediction of Communication Effectiveness During Media Skills Training Using Commercial Automatic Non-verbal Recognition Systems

It is well recognised that social signals play an important role in communication
effectiveness. Observation of videos to understand non-verbal behaviour is timeconsuming and limits the potential to incorporate detailed and accurate feedback
of this behaviour in practical applications such as communication skills training or
performance evaluation. The aim of the current research is twofold: (1) to investigate
whether off-the-shelf emotion recognition technology can detect social signals in media
interviews and (2) to identify which combinations of social signals are most promising
for evaluating trainees’ performance in a media interview. To investigate this, nonverbal signals were automatically recognised from practice on-camera media interviews
conducted within a media training setting with a sample size of 34. Automated nonverbal signal detection consists of multimodal features including facial expression,
hand gestures, vocal behaviour and ‘honest’ signals. The on-camera interviews were
categorised into effective and poor communication exemplars based on communication
skills ratings provided by trainers and neutral observers which served as a ground truth.
A correlation-based feature selection method was used to select signals associated with
performance. To assess the accuracy of the selected features, a number of machine
learning classification techniques were used. Naive Bayes analysis produced the best
results with an F-measure of 0.76 and prediction accuracy of 78%. Results revealed
that a combination of body movements, hand movements and facial expression are
relevant for establishing communication effectiveness in the context of media interviews.
The results of the current study have implications for the automatic evaluation of
media interviews with a number of potential application areas including enhancing
communication training including current media skills training