de Boer J N, Voppel A E, Brederoo S G, Schnack H G, Truong K P, Wijnen F N K, Sommer I E C
Department of Biomedical Sciences of Cells and Systems and Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
Department of Psychiatry, University Medical Center Utrecht, Utrecht University & University Medical Center Utrecht Brain Center, Utrecht, the Netherlands.
Psychol Med. 2023 Mar;53(4):1302-1312. doi: 10.1017/S0033291721002804. Epub 2021 Aug 4.
Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms.
Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition.
The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive negative symptoms could be classified with an accuracy of 74.2%.
Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.
临床医生通常将言语印象作为精神状态检查的一个要素。在精神分裂症谱系障碍中，言语描述用于评估精神病性症状的严重程度。在本研究中，我们评估了声学言语参数在精神分裂症谱系障碍中的诊断价值，以及其在识别阳性和阴性症状方面的价值。
在关于中性话题的半结构化访谈中，从142例精神分裂症谱系障碍患者和142例匹配的对照者获取言语样本。使用阳性和阴性症状量表（PANSS）将患者分为以阳性或阴性症状为主。使用OpenSMILE提取声学参数，采用扩展的日内瓦简约声学参数集，其中包括对音高（F0）、语音质量和停顿的标准化分析。将言语参数输入具有留十法交叉验证的随机森林算法，以评估其在精神分裂症谱系诊断和PANSS亚型识别中的价值。
仅基于言语参数，机器学习言语分类器在区分精神分裂症谱系障碍患者和对照者时的准确率达到86.2%。以阳性或阴性症状为主的患者分类准确率为74.2%。
我们的结果表明，自动提取的言语参数可用于准确区分精神分裂症谱系障碍患者和健康对照者，以及区分以阳性或阴性症状为主的患者。因此，鉴于言语技术领域的方法易于在不同样本间进行比较和重复，它提供了一种标准化、强大的工具，在诊断和鉴别方面具有很高的临床应用潜力。
