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Analysis of spectral properties of speech for detecting suicide risk and impact of gender specific differences.

机译：分析语音频谱特性，以检测自杀风险和性别差异的影响。

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摘要

Depression is a potentially life threatening mood disorder which affects many people. Two thirds of the people with depression don't realize that depression is a treatable illness, only 50% of people diagnosed with major depression receive any kind of treatment, and only 20% of them get treatment. Depression can lead to suicidal behavior. It is very important to realize that depression is a treatable disorder and suicide is a preventable act. A recent research study reported a frightening result which was that 48% of patients who have suicidal ideations and 24% of those who have committed suicide did not receive any care or even perceive the need for care. Therefore, it is very important to evaluate a patient's psychological state and to evaluate a depressed patient's risk of committing suicide, since suicide may be prevented by the psychiatric help. A unique challenge is discriminating the high risk suicidal (HR) patients from the depressed (DP) patients and this dissertation is focused on tackling this challenge.;In this dissertation, two different types of audio recordings from the depressed patients (diagnosed with depression), the high risk suicidal patients (diagnosed with high risk suicide), and the remitted patients (diagnosed with remission from the depression) were gathered and analyzed. One type is audio recordings that were gathered from the clinical interviews (interview session); the other one is gathered while the patients were reading a predetermined passage (reading session).;This dissertation presents three different studies. In the first study, mel-frequency cepstral coefficients (MFCCs) are used to estimate suicidal risk using different numbers of MFCCs with and without environmental compensation. A different approach is proposed to maximize the classification rates of discriminating the high risk suicidal patients from the depressed patients using fewer coefficients. The aim of this research is estimating the suicidal risk using MFCCs with high classification rates and the results show that the MFCCs are useful indicators for DP-HR discrimination.;In the second study, we propose various approaches to maximize the classification rates of discriminating the high risk suicidal patients from the depressed patients using power spectral density features. In earlier studies, 4 fixed energy bands which are uniformly placed band edges in the 0--2000 Hz frequency range (0--500 Hz, 500--1000 Hz, 1000--1500 Hz, 1500--2000 Hz) were analyzed. In this study, various optimization techniques are used which are increasing the number of energy bands, increasing the energy band range, increasing the energy band number & range, exponential band edges, exponential band edges & increasing the energy band range, non-uniform band edges, and finally non-uniform band edges & increasing the energy band range. It is found that these approaches provide better classification rates for discriminating the high risk suicidal patients from the depressed patients.;In the last study, gender specific differences on optimized energy bands are investigated. There exist statistically significant gender differences in the Depressed (DP) and the High Risk Suicidal (HR) pairwise group during the interview and reading sessions. 14 statistically significant features are found during the interview session, and 4 statistically significant features are found during the reading session. There are no statistically significant gender differences in the High Risk Suicidal-Remitted (HR-RM) pairwise group during the interview session and during the reading session. There exist statistically significant gender differences in the Depressed (DP) and the Remitted (RM) pairwise group during the interview and reading sessions. 26 statistically significant features are found during the interview session, and 2 statistically significant features are found during the reading session. Spontaneous speech (interview session) is more effective for revealing gender differences than the controlled reading speech (reading session).
机译：抑郁症是一种潜在的威胁生命的情绪障碍，影响许多人。三分之二的抑郁症患者没有意识到抑郁症是可以治疗的疾病，只有50％的被诊断患有严重抑郁症的人接受过任何治疗，只有20％的患者得到了治疗。抑郁会导致自杀行为。认识到抑郁症是可以治疗的疾病，自杀是可以预防的行为，这一点非常重要。最近的一项研究报告显示出令人恐惧的结果，即48％的具有自杀意识的患者和24％的自杀患者没有得到任何护理，甚至没有意识到需要护理。因此，评估患者的心理状态和评估抑郁患者自杀的风险非常重要，因为精神病学帮助可以预防自杀。一个独特的挑战是将高风险自杀（HR）患者与抑郁症（DP）患者区分开，并且本论文着重于应对这一挑战。收集并分析了高风险自杀患者（诊断为高风险自杀）和缓解患者（诊断为抑郁症缓解）。一种类型是从临床访谈（访谈环节）收集的录音；另一篇是在患者阅读预定段落（阅读课）时收集的。本论文提出了三项不同的研究。在第一个研究中，使用梅尔频率倒谱系数（MFCC）来估计使用具有和不具有环境补偿的不同数量的MFCC的自杀风险。提出了一种不同的方法，以使用较少的系数来最大化区分高风险自杀患者和抑郁患者的分类率。这项研究的目的是使用具有较高分类率的MFCC来估计自杀风险，结果表明MFCC是DP-HR歧视的有用指标。高危自杀患者从抑郁患者使用功率谱密度功能。在较早的研究中，分析了4个固定能带，它们是在0--2000 Hz频率范围（0--500 Hz，500--1000 Hz，1000--1500 Hz，1500--2000 Hz）内均匀放置的频带边缘。在这项研究中，使用了各种优化技术，这些技术包括增加能带数量，增加能带范围，增加能带数量和范围，指数带边缘，指数带边缘和增加能带范围，非均匀带。边缘，最后是不均匀的带边缘，并增加了能带范围。发现这些方法为区分高危自杀患者和抑郁抑郁患者提供了更好的分类率。在最近的研究中，研究了最佳能带上的性别特异性差异。在访谈和阅读过程中，抑郁（DP）和高危自杀（HR）配对组在统计学上存在明显的性别差异。在面试期间发现了14个具有统计意义的特征，在阅读期间发现了4个具有统计意义的特征。在访谈期间和阅读期间，高风险自杀缓解率（HR-RM）配对组在统计学上没有性别差异。在面试和阅读过程中，抑郁（DP）和缓解（RM）的成对组在统计学上存在显着的性别差异。在访谈期间发现了26个具有统计意义的特征，而在阅读期间发现了2个具有统计意义的特征。自发性言语（面试环节）比控制阅读言语（阅读环节）更能揭示性别差异。

著录项

  • 作者

    Keskinpala, Hande Kaymaz.;

  • 作者单位

    Vanderbilt University.;

  • 授予单位 Vanderbilt University.;
  • 学科 Health Sciences Mental Health.;Engineering Biomedical.;Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 619 p.
  • 总页数 619
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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