Introducing emotions to the modeling of intra- and inter-personal influences in parent-adolescent conversations

An understanding of the dynamics underlying emotional interactions between speakers is essential to the design of effective conversational strategies for interviews, mental health therapies, teaching and counseling, as well as the design of naturalistic human-machine communication systems. The present study introduces a new approach to the modeling of emotional influences during parent-adolescent conversations. The proposed Dynamic Influence Model (DIM) estimates the joint conditional probabilities of speaker's states as a linear combination of simpler inter- and intra-speaker conditional probabilities. Contrary to the previously existing Influence Models (IMs), the DIM's coefficients are given not as static, constant values but as dynamically changing functions of the time delay between the current and the previous state. The speaker's states were annotated using four labels (speech with positive emotion, speech with negative emotion, emotionally neutral speech and silence with undefined emotion). Experimental results based on the audio recordings of 63 different naturalistic (not acted) parent-adolescent conversations showed that the proposed method leads to psychologically plausible observations. It was also demonstrated that the proposed DIM can achieve up to 20% higher accuracy of discriminating between emotional influence patterns of parents and adolescents when compared to the previously used static IM.