[论文解读] "Am I A Good Therapist?" Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies.

Nikolaos Flemotomos, Víctor Martínez+14 more|arXiv (Cornell University)|Feb 22, 2021

Speech and dialogue systems参考文献 67被引用 6

一句话总结

本文提出了一种自动化系统，通过分析原始音频记录来评估心理治疗技能，特别是 motivational interviewing（ motivational interviewing）中的治疗师行为、语言使用和会话动态。该工具已部署于5,000场真实会话中，可提供详细、实时的反馈，以提升治疗师培训质量与临床实践水平。

ABSTRACT

With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care, in order to assist in training, supervision, and quality assurance of services. Traditionally, quality assessment is addressed by human raters who evaluate recorded sessions along specific dimensions, often codified through constructs relevant to the approach and domain. This is however a cost-prohibitive and time-consuming method which leads to poor feasibility and limited use in real-world settings. To facilitate this process, we have developed an automated competency rating tool able to process the raw recorded audio of a session, analyzing who spoke when, what they said, and how the health professional used language to provide therapy. Focusing on a use case of a specific type of psychotherapy called Motivational Interviewing, our system gives comprehensive feedback to the therapist, including information about the dynamics of the session (e.g., therapist's vs. client's talking time), low-level psychological language descriptors (e.g., type of questions asked), as well as other high-level behavioral constructs (e.g., the extent to which the therapist understands the clients' perspective). We describe our platform and its performance, using a dataset of more than 5,000 recordings drawn from its deployment in a real-world clinical setting used to assist training of new therapists. We are confident that a widespread use of automated psychotherapy rating tools in the near future will augment experts' capabilities by providing an avenue for more effective training and skill improvement and will eventually lead to more positive clinical outcomes.

研究动机与目标

• 解决基于人工的心理治疗技能评估成本高昂且可行性低的问题。
• 开发一种自动化系统，能够分析录音会话以评估临床能力。
• 在培训与督导过程中，为治疗师提供可操作的、数据驱动的反馈。
• 通过可扩展的技术驱动评估，提升培训效率与临床结果。

提出的方法

• 系统处理原始音频记录，通过自动语音识别技术检测说话人轮换并转录语音内容。
• 应用自然语言处理技术，将治疗师语言分类为低层级描述符，如问题类型与情感基调。
• 计算会话层面的指标，包括治疗师与来访者说话时间及互动平衡。
• 利用语言学与话语特征，推断高层次治疗行为，如共情、复述与 motivational interviewing 技术。
• 将这些分析整合至综合反馈仪表板，供治疗师使用。
• 该系统基于超过5,000场临床实践中获取的真实会话数据集进行训练与验证。

实验结果

研究问题

• RQ1 自动化语音与语言技术能否可靠评估真实会话中 motivational interviewing 的核心组成部分？
• RQ2 自动化系统在多大程度上能准确检测治疗师行为，如复述、开放式提问与共情？
• RQ3 自动化反馈在多大程度上可改善治疗师培训与技能发展？
• RQ4 自动化评估工具能否在真实临床环境中实际部署，以支持督导与质量保障？

主要发现

• 该系统成功分析了超过5,000场真实会话录音，展示了在临床部署中的可扩展性。
• 它准确识别了关键治疗行为，如复述、开放式问题与以来访者为中心的语言。
• 该工具提供了关于会话动态的详细反馈，包括治疗师与来访者说话时间比例。
• 系统通过语言模式检测到高层次构念，如治疗师共情与对来访者视角的理解。
• 生成的反馈具有可操作性且与培训和督导相关，有助于技能发展。
• 该平台有望通过可扩展的自动化评估，显著提升培训效率与临床质量。