Biomedical Informatics and Medical Education.
Engineering (BRiTE) Center, Psychiatry and Behavioral Sciences; University of Washington, Seattle, WA.
AMIA Annu Symp Proc. 2021 Jan 25;2020:1315-1324. eCollection 2020.
Thought disorder (TD) as reflected in incoherent speech is a cardinal symptom of schizophrenia and related disorders. Quantification of the degree ofTD can inform diagnosis, monitoring, and timely intervention. Consequently, there has been an interest in applying methods ofdistributional semantics to quantify incoherence ofspoken language. Prior studies have generally involved few participants and utilized speech data collected in on-site structured interviews. In this paper we conduct a comprehensive evaluation ofapproaches to quantify incoherence using distributional semantics, including a novel variant that measures the global coherence oftext. This evaluation is conducted in the context of "audio diaries" collected from participants experiencing auditory verbal hallucinations using a smartphone application. Results reveal our novel global coherence metric using the centroid (weighted vector average) outperforms established approaches in their agreement with human annotators, supporting their preferential use in the context of short recordings ofunstructured and largely spontaneous speech.
思维障碍(TD)表现为言语不连贯,是精神分裂症及相关障碍的主要症状。TD 程度的量化可以为诊断、监测和及时干预提供信息。因此,人们一直有兴趣应用分布语义学方法来量化口语的不连贯性。先前的研究通常涉及较少的参与者,并利用现场结构化访谈中收集的语音数据。在本文中,我们全面评估了使用分布语义学量化不连贯性的方法,包括一种新的变体,用于衡量文本的整体连贯性。该评估是在使用智能手机应用程序收集经历听觉言语幻觉的参与者的“音频日记”的背景下进行的。结果表明,我们使用质心(加权向量平均值)的新全局连贯性指标在与人类注释者的一致性方面优于已有的方法,支持它们在非结构化和大部分自发言语的短录音背景下的优先使用。