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A technology prototype system for rating therapist empathy from audio recordings in addiction counseling.
PeerJ Computer Science ( IF 3.8 ) Pub Date : 2017-03-14 , DOI: 10.7717/peerj-cs.59
Bo Xiao 1 , Chewei Huang 1 , Zac E Imel 2 , David C Atkins 3 , Panayiotis Georgiou 1 , Shrikanth S Narayanan 1
Affiliation  

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.

中文翻译:

一种技术原型系统,用于根据成瘾咨询中的录音对治疗师的移情进行评级。

扩大心理治疗服务(如成瘾咨询服务)是一项重要的社会需求。一项挑战是确保治疗的质量,因为人工观察评估的费用很高。这项工作提出了一种基于语音技术的系统,该系统可以从心理治疗互动的录音中自动评估治疗师的移情能力(一种关键的治疗质量指标)。我们设计了一种语音处理系统,其中包括语音活动检测和区分模块,自动语音识别器以及说话者角色匹配模块,以提取治疗师的语言提示。我们采用了最大熵模型,最大似然语言模型和格点记录方法来表征高共情语言和低共情语言。我们使用从这些模型获得的话语水平证据来估计治疗阶段的共情代码。我们的实验表明,全自动系统在专家注释的共情代码与机器得出的估计之间实现了0.643的相关性,在对高共情和低共情进行分类时,准确度为81%,而相比之下,相关性为0.721,准确度为86%使用手动成绩单进行oracle设置。结果表明,该系统提供了有助于自动质量保险和治疗师培训的有用信息。使用手动笔录在oracle设置中具有721相关性和86%的准确性。结果表明,该系统提供了有助于自动质量保险和治疗师培训的有用信息。使用手动笔录在oracle设置中具有721相关性和86%的准确性。结果表明,该系统提供了有助于自动质量保险和治疗师培训的有用信息。
更新日期:2019-11-01
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