Among the characteristic symptoms of schizophrenia, negative symptoms are a category. The existence of negative symptoms results in a diminution of normal behaviors and functions for schizophrenic patients. In this research, we focus on the affective flattening of negative symptoms to explore potential biomarkers to achieve automatic diagnosis. This work proposes an automatic procedure for detecting the negative symptoms of schizophrenic patients based on speech signal processing. In this procedure, three features are initially proposed: the symmetric spectral difference level (SSDL), quantization error and vector angle (QEVA), and standard dynamic volume value (SDVV). The SSDL feature is designed with the aim of emphasizing spectral differences that contribute to the evaluation of emotional richness. The QEVA is proposed to reflect the variations in tone using one cumulative error indicator and one variation evaluation indicator. The SDVV feature aims to represent the modulation of speech intensity, considering the speaking behavior of schizophrenic patients. Experiments evaluating the discriminative capabilities of the three features are conducted using a speech database collected from 56 participants (28 schizophrenic patients and 28 healthy controls). The classifier employed in these experiments is a simple decision tree. Three other binary classifiers [linear discrimination (LD), logistic regression (LR), and a support vector machine (SVM)] are also tested to compare their performances with the decision tree in this work. Based on the decision tree classifier, the discrimination accuracy levels of schizophrenic patients and control subjects using the SDLL, QEVA, and SDVV features are in the range of 80.5%–83%, 65%–73%, and 87%– 91.5%, respectively. When the three features are combined, the best discrimination accuracy of schizophrenic patients and control subjects is 98.2%, with an area under the receiver operations characteristic curve (AUC) value of 98%. Note to Practitioners —This article is motivated by the problems of the diagnosis of negative symptoms in schizophrenia and the timely monitoring of schizophrenic patients. In clinic, there exists a high patient-to-clinician ratio and the diagnostic result depends on the subjective experience of the clinician. It is necessary to develop an automatic and objective procedure for the diagnosis and monitoring of schizophrenic patients. Speech disorders are among the salient characteristics of negative symptoms. In this work, an automatic procedure for detecting the negative symptoms based on speech signal processing is proposed. This automatic procedure is achieved based on three newly presented acoustic features involving the characteristics of schizophrenic speech, which consider the speaking behavior of schizophrenic patients. This automatic procedure and the information of the three acoustic features may serve as an aid to clinicians and could potentially help them in providing better monitoring of schizophrenic patients.

中文翻译：

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通过与情感扁平化相关的声学测量特征自动检测精神分裂症的阴性症状

在精神分裂症的特征性症状中，阴性症状是一类。负面症状的存在导致精神分裂症患者的正常行为和功能减少。在这项研究中，我们专注于负面症状的情感扁平化，以探索潜在的生物标志物，以实现自动诊断。这项工作提出了一种基于语音信号处理的检测精神分裂症患者阴性症状的自动程序。在此过程中，最初提出了三个功能：对称频谱差异水平（SSDL），量化误差和矢量角度（QEVA）和标准动态体积值（SDVV）。SSDL功能的设计旨在强调有助于评估情感丰富度的频谱差异。建议使用一个累积误差指标和一个变化评估指标来反映QEVA，以反映色调的变化。考虑到精神分裂症患者的言语行为，SDVV功能旨在表示语音强度的调制。使用从56位参与者（28位精神分裂症患者和28位健康对照）收集的语音数据库进行了评估这三种功能的判别能力的实验。这些实验中使用的分类器是一个简单的决策树。还测试了其他三个二进制分类器[线性判别（LD），逻辑回归（LR）和支持向量机（SVM）]，以将其性能与决策树进行比较。基于决策树分类器，使用SDLL，QEVA和SDVV功能的精神分裂症患者和对照组的分辨准确度分别在80.5％–83％，65％–73％和87％–91.5％之间。当将这三个特征组合在一起时，精神分裂症患者和对照对象的最佳区分准确度是98.2％，接收器操作特征曲线（AUC）值下的面积为98％。执业者须知 -本文的主题是精神分裂症的阴性症状的诊断和对精神分裂症患者的及时监测。在临床中，患者与临床医生的比例很高，诊断结果取决于临床医生的主观经验。有必要开发一种自动客观的方法来诊断和监测精神分裂症患者。言语障碍是阴性症状的主要特征之一。在这项工作中，提出了一种基于语音信号处理的自动检测阴性症状的程序。该自动程序是基于三个新出现的涉及精神分裂症患者语音特征的声学特征而实现的，这些特征考虑了精神分裂症患者的言语行为。