Objective prediction of social skills level for automated social skills training using audio and text information

Abstract

Although Social Skills Training is a well-known effective method to obtain appropriate social skills during daily communication, getting such training is difficult due to a shortage of therapists. Therefore, automatic training systems are required to ameliorate this situation. To fairly evaluate social skills, we need an objective evaluation method. In this paper, we utilized the second edition of the Social Responsiveness Scale (SRS-2) as an objective evaluation metric and developed an automatic evaluation system using linear regression with multi-modal features. We newly adopted features including 28 audio features and BERT-based sequential similarity (seq-similarity), which indicates how well the meaning of users remains consistent within their utterances. We achieved a 0.35 Pearson correlation coefficient for the SRS-2's overall score prediction and 0.60 for the social communication score prediction, which is a treatment sub-scale score of SRS-2. This experiment shows that our system can objectively predict the levels of social skills. Please note that we only evaluated the system on healthy subjects since this study is still at the feasibility phase. Therefore, further evaluation of real patients is needed in future work.