Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research

Speaker diarization is the practice of determining who speaks when in audio recordings. Psychotherapy research often relies on labor intensive manual diarization. Unsupervised methods are available but yield higher error rates. We present a method for supervised speaker diarization based on random forests. It can be considered a compromise between commonly used labor-intensive manual coding and fully automated procedures. The method is validated using the EMRAI synthetic speech corpus and is made publicly available. It yields low diarization error rates (M: 5.61%, STD: 2.19). Supervised speaker diarization is a promising method for psychotherapy research and similar fields

Withdrawal ruptures in adolescents with borderline personality disorder psychotherapy are marked by increased speech pauses-can minimal responses be automatically detected?

Alliance ruptures of the withdrawal type are prevalent in adolescents with borderline personality disorder (BPD). Longer speech pauses are negatively perceived by these patients. Safran and Muran's rupture model is promising but its application is very work intensive. This workload makes research costly and limits clinical usage. We hypothesised that pauses can be used to automatically detect one of the markers of the rupture model i.e. the minimal response marker. Additionally, the association of withdrawal ruptures with pauses was investigated. A total of 516 ruptures occurring in 242 psychotherapy sessions collected in 22 psychotherapies of adolescent patients with BPD and subthreshold BPD were investigated. Trained observers detected ruptures based on video and audio recordings. In contrast, pauses were automatically marked in the audio-recordings of the psychotherapy sessions and automatic speaker diarisation was used to determine the speaker-switching patterns in which the pauses occur. A random forest classifier detected time frames in which ruptures with the minimal response marker occurred based on the quantity of pauses. Performance was very good with an area under the ROC curve of 0.89. Pauses which were both preceded and followed by therapist speech were the most important predictors for minimal response ruptures. Research costs can be reduced by using machine learning techniques instead of manual rating for rupture detection. In combination with other video and audio derived features like movement analysis or automatic facial emotion detection, more complete rupture detection might be possible in the future. These innovative machine learning techniques help to narrow down the mechanisms of change of psychotherapy, here specifically of the therapeutic alliance. They might also be used to technologically augment psychotherapy training and supervision

The impact of outcome expectancy on therapy outcome in adolescents with borderline personality disorder.

BACKGROUND Outcome expectancy has been found to be a significant predictor of psychotherapy outcome. However, given that severity, chronicity and comorbidity are moderators of outcome expectancy, it is important to provide evidence of whether the same holds true in clinical conditions marked by these attributes, such as in borderline personality disorder (BPD). The aim of the present study was to investigate the role of patients' outcome expectancy in adolescents undergoing early intervention for BPD using pre-post difference of psychosocial functioning as outcome. METHODS Forty-four adolescent BPD patients were treated with Dialectical Behavior Therapy for Adolescents (DBT-A) or Adolescent Identity Treatment (AIT). We investigated the effect of outcome expectancy on outcome with type of treatment as moderator. Based on the relevant literature, we assess the correlation between outcome expectancy and pretreatment symptomatology, namely BPD severity, personality functioning, childhood trauma and depression. RESULTS The results showed a significant effect of expectancy on outcome (stand. β = 0.30, p = 0.020) above autoregression. ANOVA analysis revealed no difference between the two treatments. Further, results indicate that pretreatment symptomatology, i.e., depression, childhood trauma and personality functioning dimensions self-direction and intimacy, are associated with early treatment expectancy. CONCLUSION Outcome expectancy as a common factor plays a key role in successful psychotherapy with adolescent BPD patients. Elevated pretreatment depression, childhood trauma and impairment in personality functioning dimensions self-direction and intimacy are risk factors associated with lower expectancy. Low outcome expectancy should be addressed in early psychotherapy to improve the therapeutical process

Withdrawal ruptures in adolescents with borderline personality disorder psychotherapy are marked by increased speech pauses–can minimal responses be automatically detected?

Alliance ruptures of the withdrawal type are prevalent in adolescents with borderline personality disorder (BPD). Longer speech pauses are negatively perceived by these patients. Safran and Muran’s rupture model is promising but its application is very work intensive. This workload makes research costly and limits clinical usage. We hypothesised that pauses can be used to automatically detect one of the markers of the rupture model i.e. the minimal response marker. Additionally, the association of withdrawal ruptures with pauses was investigated. A total of 516 ruptures occurring in 242 psychotherapy sessions collected in 22 psychotherapies of adolescent patients with BPD and subthreshold BPD were investigated. Trained observers detected ruptures based on video and audio recordings. In contrast, pauses were automatically marked in the audio-recordings of the psychotherapy sessions and automatic speaker diarisation was used to determine the speaker-switching patterns in which the pauses occur. A random forest classifier detected time frames in which ruptures with the minimal response marker occurred based on the quantity of pauses. Performance was very good with an area under the ROC curve of 0.89. Pauses which were both preceded and followed by therapist speech were the most important predictors for minimal response ruptures. Research costs can be reduced by using machine learning techniques instead of manual rating for rupture detection. In combination with other video and audio derived features like movement analysis or automatic facial emotion detection, more complete rupture detection might be possible in the future. These innovative machine learning techniques help to narrow down the mechanisms of change of psychotherapy, here specifically of the therapeutic alliance. They might also be used to technologically augment psychotherapy training and supervision

Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study.

BACKGROUND New advances in the field of machine learning make it possible to track facial emotional expression with high resolution, including micro-expressions. These advances have promising applications for psychotherapy research, since manual coding (e.g., the Facial Action Coding System), is time-consuming. PURPOSE We tested whether this technology can reliably identify in-session emotional expression in a naturalistic treatment setting, and how these measures relate to the outcome of psychotherapy. METHOD We applied a machine learning emotion classifier to video material from 389 psychotherapy sessions of 23 patients with borderline personality pathology. We validated the findings with human ratings according to the Clients Emotional Arousal Scale (CEAS) and explored associations with treatment outcomes. RESULTS Overall, machine learning ratings showed significant agreement with human ratings. Machine learning emotion classifiers, particularly the display of positive emotions (smiling and happiness), showed medium effect size on median-split treatment outcome (d = 0.3) as well as continuous improvement (r = 0.49, p < 0.05). Patients who dropped out form psychotherapy, showed significantly more neutral expressions, and generally less social smiling, particularly at the beginning of psychotherapeutic sessions. CONCLUSIONS Machine learning classifiers are a highly promising resource for research in psychotherapy. The results highlight differential associations of displayed positive and negative feelings with treatment outcomes. Machine learning emotion recognition may be used for the early identification of drop-out risks and clinically relevant interactions in psychotherapy

Umsetzung der Energiestrategie 2050: Herausforderungen und Chancen für Staat und Wirtschaft

Sammelband der Reihe "Energy Governance Working Paper" Nr. 1 bis 7Die Energiestrategie 2050 des Bundes definiert anspruchsvolle Ziele. Für deren Erreichung hat der Bundesrat daher unter anderem den Aktionsplan Energieforschung ins Leben gerufen. Dazu wurden acht sogenannte SCCERs, Swiss Competence Center for Energy Research, initiiert, in denen hochschulübergreifend angewandte Energie-Forschung betrieben wird. Die Zürcher Hochschule für Angewandte Wissenschaften (ZHAW) ist an vier dieser acht SCCERs aktiv beteiligt. Die ZHAW hat diese Aufgabe zum Anlass genommen, Energieforschung zum strategischen Schwerpunkt der gesamten Fachhochschule zu erklären und in allen Departementen Kompetenzaufbauprojekte zu starten. Der vorliegende Sammelband präsentiert die ersten Ergebnisse dieser Kompetenzaufbauprojekte an der School of Management and Law, wobei zwei dieser Projekte in Zusammenarbeit mit Forschern aus den Departementen Angewandte Linguistik und School of Engineering erfolgten. Dabei wurden die Herausforderungen und Chancen, die sich für Staat und Wirtschaft aus der Umsetzung der Energiestrategie 2050 ableiten, auf verschiedenen Ebenen betrachtet: die Schweiz im internationalen Vergleich, Besonderheiten der Führung von EVUs, rechtliche und ökonomische Rahmenbedingungen und die Gestaltung der Energie-Zukunft in Schweizer Städten

Assessment of affective and conversational trajectories in psychotherapy with adolescents suffering from borderline personality disorder

Recently, machine learning methodologies and affective computing have become more popular in the field of psychotherapy research (AaVes-van Doorn et al., 2020; Poria et al., 2017). Methods facilitate video, audio and text processing in order to extract verbal (Goldberg et al., 2020) and nonverbal information (facial expression: Arango et al., 2019; postures: Zhang et al., 2018; paralanguage: Crangle et al., 2019). A promising application for automated streams of nonverbal information is emotion recognition (Halfon et al., 2020; Sharma & Dhall, 2021). .e drastic rise of possibilities and the eclectic use of symbolic information streams begs for anchoring theory to inform meaning making of nonverbal phenomena. Most publications study the mutual interdependence of nonverbal signals in patient and therapist (synchrony, attunement, concordance, coordination), taking a firm stand in generalizing theories (common factor theory, communication theory, information processing theory, self- organisation theory), implying that studied processes of nonverbal exchange translate to transtheoretical concepts of psychotherapy research and human interaction in general (Koole & Tschacher, 2016; Laroche et al., 2014; Salvatore et al., 2015). On a more fine-grained conceptual level, empathy, emotion regulation and quality of therapeutic (working) relationship (alliance) are proposed as correlates (Imel et al., 2014; Reich et al., 2014; Soma et al., 2020). In this dissertation, I illuminate the use of nonverbal signals from a different perspective. Being a practicing therapist myself, I anchor my thoughts in a clinical standpoint, applying theory on mechanisms of change in relational psychoanalysis (Adolescent Identity Treatment: Foelsch et al., 2014; Transference Focused .erapy: Levy et al., 2006) relevant to the psychiatric understanding of (borderline) personality disorders. By collapsing assumptions of relational psychoanalysis (Tufekcioglu & Muran, 2014) and the events paradigm (Timulak, 2010), psychotherapy is conceptualized as the mutual communication and regulation of self-states in an ongoing negotiation process that brings forth key events. More precisely, using object-relations theory (Kernberg, 1995), key events can be defined as episodes of integrative work with the dominant object relation dyads. .e instrumental involvement of nonverbal communication streams in this process is discussed (Dreyer, 2018; Mac Cormack, 1997). On the conceptual level, it is possible to find common ground between emotion regulation (Campos et al., 2011) and alliance negotiation (Eubanks et al., 2019) as they both emerge as nonverbal communication acts in moments of therapeutic work, moving towards the clinical goal of identity integration (Jung et al., 2013; Schlüter-Müller et al., 2020; Schmeck et al., 2013). I discuss how the automatic assessment of nonverbals can help recognizing these key moments. Interpersonal coordination processes have been proposed as correlates for emotion regulation and alliance negotiation. I highlight problems with the concrete adaptation of coordination assessment under the assumptions of the events paradigm and how they can be overcome by the perspective of relational approaches

Multiple MRIs demagnetized an internal BAHA magnet – An enriching case for the everyday practice

AbstractWe present a Baha patient with demagnetization after repeated magnetic resonance imagings (MRIs) and demonstrate a novel way of monitoring magnet adherence. A 53-year-old man with a Baha Attract System reported reduced holding force of the external audio processor after nine MRIs (eight at 1.5 T and one at 3 T). Subsequently, the original external magnet was replaced by a stronger magnet without benefit. After three additional MRIs at 1.5 T, a total loss of magnetic adherence was reported. During revision surgery replacing the internal magnet, we used three different methods to document the magnetic field of the old and new internal magnet; a gaussmeter, a flux detector film and in-situ determination of the minimum adhesive magnetic force. Measurements performed with the gaussmeter served as the gold standard and were confirmed by the other two methods. The application of MR imaging at 3 T in patients with a Baha system is off-label use and initiates magnetic-implant damage, while repeated MRI at 1.5 T scans seems to be harmless. To document potential demagnetization, a flux detector film can be used in daily practice due to its simplicity of application and broad availability

Machine Learning Facial Emotion Recognition in Psychotherapy Research. A useful approach?

Background: Manual coding of facial emotion expression, e.g.using the Facial Action Coding System (FACS), can be very time consuming. For psychotherapy research the change in emotion expressionand microexpressions over time is relevant. Hence, automatic emotionrecognition may be a promising tool. Method: We apply a Convolutionary Neural Network (CNN) for emotion recognition to video material from 389 psychotherapy sessions of 23 patients with borderline personality pathology. We cross-validate the findings with human ratings accordingto the Clients Emotional Arousal Scale (CEAS) and the outcome of psychotherapy. Results: Overall, machine learning ratings show substantial,however, numerically low agreement with human ratings, particularly with overall (non-specific) emotional arousal. Machine learning emotion recognition shows substantial predictive validity for therapy outcome, in particular the display of positive emotions (smiling and happiness). Discussion: Machine learning is a highly promising resource for tracking change in emotional expression over time. The results highlight thedifferential association of displayed positive and negative feelings to the treatment outcome

Alliance Ruptures and Resolutions in Personality Disorders

This review provides an overview of the state of research on alliance ruptures and resolutions in the treatment of personality disorders (PDs). We discuss frequently used instruments to measure alliance ruptures and resolutions. We discuss the effectiveness of rupture resolution processes and highlight possible avenues for research to explore. Innovative assessments with the potential to reveal the link of ruptures and resolutions and mechanisms of psychotherapeutic change are discussed.; The assessment of alliance rupture and resolutions is heterogeneous. Instruments vary largely with respect to a direct or indirect assessment, the time resolution of assessment (integral therapy, phase, session, event), session sampling strategy and perspectives (patient, therapist, observer). The heterogeneity in the instruments and study designs impedes comparability and interpretation of the findings. Results support the hypothesis that ruptures are more frequent in PD. Results also point towards beneficial effects of rupture resolution patterns, early alliance quality, and resolution complexity. Few studies control findings for pretreatment factors. Evidence points to the direction that rupture resolution processes can be considered a general principle of change in the treatment of PD. The concept of alliance ruptures and resolutions provides a useful tool for the management of the therapeutic alliance and its moments of deteriorations throughout the treatment course. Dimensional pretreatment personality functioning is considered a key variable in future studies to highlight what works for whom