Abstract
It is well recognised that social signals play an important role in communication effectiveness. Observation of videos to understand non-verbal behaviour is time-consuming 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, non-verbal signals were automatically recognised from practice on-camera media interviews conducted within a media training setting with a sample size of 34. Automated non-verbal 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.
Highlights
Skilful communication in media interviews is important in a range of organisations and job roles
The results suggest that body positioning, facial expressions, vocal signals and hand gestures are all relevant for the context of media interviews
In this paper we investigated whether social signals can be detected in a dyadic interaction using commercial automated technology and whether good interviews could be distinguished from poorer interviews on the basis of such signals
Summary
Skilful communication in media interviews is important in a range of organisations and job roles. It is important that accurate and objective observations of non-verbal cues are incorporated into assessment of media performance and training interventions to improve performance. We briefly introduce research using automated detection of non-verbal signals and limitations in previous research; we introduce the reader to the aims and objectives of the current research. Studies of non-verbal signals show that communication is typically characterised by the complex interplay of reciprocal signals between interlocutors (Knapp et al, 2013). In evolutionary terms, displaying emotions benefits both senders and receivers in social interactions. These signals are communicated via multiple channels; such as facial expressions, vocal behaviour (i.e., tone of voice and vocal bursts), gestures and posture (Adams and Kveraga, 2015)
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