A technology prototype system for rating therapist empathy from audio recordings in addiction counseling

Scaling up psychotherapy services such as for addiction counseling is a critical societal need. One challenge is ensuring quality of therapy, due to the heavy cost of manual observational assessment. This work proposes a speech technology-based system to automate the assessment of therapist empathy—a key therapy quality index—from audio recordings of the psychotherapy interactions. We designed a speech processing system that includes voice activity detection and diarization modules, and an automatic speech recognizer plus a speaker role matching module to extract the therapist’s language cues. We employed Maximum Entropy models, Maximum Likelihood language models, and a Lattice Rescoring method to characterize high vs. low empathic language. We estimated therapy-session level empathy codes using utterance level evidence obtained from these models. Our experiments showed that the fully automated system achieved a correlation of 0.643 between expert annotated empathy codes and machine-derived estimations, and an accuracy of 81% in classifying high vs. low empathy, in comparison to a 0.721 correlation and 86% accuracy in the oracle setting using manual transcripts. The results show that the system provides useful information that can contribute to automatic quality insurance and therapist training

Use of automated coding methods to assess motivational behaviour in education

Teachers’ motivational behaviour is related to important student outcomes. Assessing teachers’ motivational behaviour has been helpful to improve teaching quality and enhance student outcomes. However, researchers in educational psychology have relied on self-report or observer ratings. These methods face limitations on accurately and reliably assessing teachers’ motivational behaviour; thus restricting the pace and scale of conducting research. One potential method to overcome these restrictions is automated coding methods. These methods are capable of analysing behaviour at a large scale with less time and at low costs. In this thesis, I conducted three studies to examine the applications of an automated coding method to assess teacher motivational behaviours. First, I systematically reviewed the applications of automated coding methods used to analyse helping professionals’ interpersonal interactions using their verbal behaviour. The findings showed that automated coding methods were used in psychotherapy to predict the codes of a well-developed behavioural coding measure, in medical settings to predict conversation patterns or topics, and in education to predict simple concepts, such as the number of open/closed questions or class activity type (e.g., group work or teacher lecturing). In certain circumstances, these models achieved near human level performance. However, few studies adhered to best-practice machine learning guidelines. Second, I developed a dictionary of teachers’ motivational phrases and used it to automatically assess teachers’ motivating and de-motivating behaviours. Results showed that the dictionary ratings of teacher need support achieved a strong correlation with observer ratings of need support (rfull dictionary = .73). Third, I developed a classification of teachers’ motivational behaviour that would enable more advanced automated coding of teacher behaviours at each utterance level. In this study, I created a classification that includes 57 teacher motivating and de-motivating behaviours that are consistent with self-determination theory. Automatically assessing teachers’ motivational behaviour with automatic coding methods can provide accurate, fast pace, and large scale analysis of teacher motivational behaviour. This could allow for immediate feedback and also development of theoretical frameworks. The findings in this thesis can lead to the improvement of student motivation and other consequent student outcomes

Crisis Intervention in Policing and Beyond: Exploring Determinants of Empathy-based Rapport-Building

This thesis documents a series of consecutive investigations into predictors of empathy and rapport during critical incidents in law enforcement and crisis intervention in general. First, an explorative inquiry (Study 1) interviewed five accredited crisis negotiators from Germany, the United Kingdom, and Canada. It performed a cognitive task analysis and found a prevalent set of (a) challenges, which are often interacting with each other, and (b) strategies, which crisis negotiators rely on during their deployment. These results equip practitioners with a better understanding of the challenges and a more effective way of utilizing strategies to effectively address them. They also point out under-researched areas in crisis negotiation literature. Second, as crisis negotiators mentioned physical and mental exhaustion to be an inhibitor of their empathetic effort across all interviews, two randomized-controlled field experiments (Study 2 & 3) tested 52 German crisis negotiators (within subjects) on their capacity to empathize when ego depleted. They both confirmed the null hypothesis: there was no statistically significant difference in the level of empathy communicated by the crisis negotiators between control and ego depletion condition. These results contribute to the current discussion surrounding the replication crisis of the ego depletion effect. Third, during the coding of the simulated crisis negotiations, crisis negotiators appeared to communicate in distinct ways that inadvertently undermined their efforts to empathize and build rapport with the subject. This serendipitous find was further investigated and validated using quantitative data analysis (Study 4). The study resulted in the identification of five cognitive biases and the insight that conventional approaches to empathy-based rapport-building have limits. The results can be effectively implemented in crisis intervention training and contribute to the theoretical discussion of empathy and the role it plays for rapport-building. Fourth, due to its conceptual relevance to empathy, projection bias was selected for further inquiry. To triangulate the findings of the qualitative data analysis (Study 4) with different methods and a different sample, an online study (Study 5) surveyed 132 crisis negotiators, police officers on patrol duty, and (non-police) crisis workers. The sample was primarily recruited from Canada and the United States. The results (a) corroborate the findings of Study 4, (b) demonstrate differences in the prevalence of projection bias between the different occupational sub-samples, and (c) provide a deeper understanding of how projection bias can undermine effective empathizing. Practical implications are discussed in terms of education and training for all professional crisis intervenors. In addition, the instrument constructed for this study contributes to future projection bias research