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

Fürer, Lukas; Schenk, Nathalie; Röth, Volker; Steppan, Martin; Schmeck, Klaus; Zimmermann, Ronan

doi:10.3389/fpsyg.2020.01726

Cited by 6 publications

(8 citation statements)

References 50 publications

(74 reference statements)

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“…The method is described in detail in the readme file and the manual [ 39 ]. The method has been used in preceding peer-reviewed publications [ 27 , 28 ]. Based on the result of the procedure, start and stop times of the pauses relative to the start of the recording were extracted.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Methodsmentioning

confidence: 99%

“…This decision tree-based method learned to classify 0.2-s windows of patient or therapist speech based on the extracted features. The source code for this method can be retrieved from github [ 43 ]. Please consider the readme file for details.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2023

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“…The RF method, based on an RF algorithm, is a machine learning technique that has been used in general in psychology research. This method is a classifier based on decision trees, which can be used in regression or classification problems ( Fürer et al, 2020 ), as used in this study.…”

Section: Methodsmentioning

confidence: 99%

The Influence of Cognitive Biases and Financial Factors on Forecast Accuracy of Analysts

et al. 2022

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The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression (MRL), k-dependence Bayesian (k-DB), and Random Forest (RF). The Bayesian inference and classification methods allow an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts (ADRs), performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing.

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“…Alternatively, machine-learning could be used to approximate critical events in psychotherapy based on automated information streams and to mark them for further manual evaluation. For example, minimal response withdrawal ruptures could be approximated by automated silence detection [40] and speaker diarization [54]. The extent of nonverbal movement synchrony has been associated with patient-rated relationship quality [51], dropout status, and improvement [55].…”

Section: Need For New Methods In Future Researchmentioning

confidence: 99%

Alliance Ruptures and Resolutions in Personality Disorders

Schenk

Fürer

Zimmermann

et al. 2020

Curr Psychiatry Rep

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Purpose of Review 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. Recent Findings 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. Summary 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.

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

Cited by 6 publications

References 50 publications

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

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

The Influence of Cognitive Biases and Financial Factors on Forecast Accuracy of Analysts

Alliance Ruptures and Resolutions in Personality Disorders

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