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

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

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

Redox Responses in Patients with Sepsis: High Correlation of Thioredoxin-1 and Macrophage Migration Inhibitory Factor Plasma Levels

Background. Redox active substances (e.g., Thioredoxin-1, Macrophage Migration Inhibitory Factor) seem to be central hubs in the septic inflammatory process. Materials and Methods. Blood samples from patients with severe sepsis or septic shock (n = 15) were collected at the time of sepsis diagnosis (t0), and 24 (t24) and 48 (t48) hours later; samples from healthy volunteers (n = 18) were collected once; samples from postoperative patients (n = 28) were taken one time immediately after surgery. In all patients, we measured plasma levels of IL-6, TRX1 and MIF. Results. The plasma levels of MIF and TRX1 were significantly elevated in patients with severe sepsis or septic shock. Furthermore, TRX1 and MIF plasma levels showed a strong correlation (t0: rsp = 0.720, ρ = 0.698/t24: rsp = 0.771, ρ = 0.949). Conclusions. Proinflammatory/~oxidative and anti-inflammatory/~oxidative agents show a high correlation in order to maintain a redox homeostasis and to avoid the harmful effects of an excessive inflammatory/oxidative response

Family composition and age at menarche: findings from the international Health Behaviour in School-Aged Children Study

This research was funded by The University of St Andrews and NHS Health Scotland.Background Early menarche has been associated with father absence, stepfather presence and adverse health consequences in later life. This article assesses the association of different family compositions with the age at menarche. Pathways are explored which may explain any association between family characteristics and pubertal timing. Methods Cross-sectional, international data on the age at menarche, family structure and covariates (age, psychosomatic complaints, media consumption, physical activity) were collected from the 2009–2010 Health Behaviour in School-aged Children (HBSC) survey. The sample focuses on 15-year old girls comprising 36,175 individuals across 40 countries in Europe and North America (N = 21,075 for age at menarche). The study examined the association of different family characteristics with age at menarche. Regression and path analyses were applied incorporating multilevel techniques to adjust for the nested nature of data within countries. Results Living with mother (Cohen’s d = .12), father (d = .08), brothers (d = .04) and sisters (d = .06) are independently associated with later age at menarche. Living in a foster home (d = −.16), with ‘someone else’ (d = −.11), stepmother (d = −.10) or stepfather (d = −.06) was associated with earlier menarche. Path models show that up to 89% of these effects can be explained through lifestyle and psychological variables. Conclusions Earlier menarche is reported amongst those with living conditions other than a family consisting of two biological parents. This can partly be explained by girls’ higher Body Mass Index in these families which is a biological determinant of early menarche. Lower physical activity and elevated psychosomatic complaints were also more often found in girls in these family environments.Publisher PDFPeer reviewe