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Affective state and voice: Cross-cultural assessment of speaking behavior and voice sound characteristics-a normative multicenter study of 577 + 36 healthy subjects

机译:情感状态和语音:跨文化评估言语行为和语音特征-对577 + 36名健康受试者的规范性多中心研究

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Methods: This project was comprised of four studies with healthy volunteers from Bristol (English: n = 117), Lausanne (French: n = 128), Zurich (German: n = 208), and Valencia (Spanish: n = 124). All samples were stratified according to gender, age, and education. The specific study design with different types of spoken text along with repeated assessments at 14-day intervals allowed us to estimate the 'natural' variation of speech parameters over time, and to analyze the sensitivity of speech parameters with respect to form and content of spoken text. Additionally, our project included a longitudinal self-assessment study with university students from Zurich (n = 18) and unemployed adults from Valencia (n = 18) in order to test the feasibility of the speech analysis method in home environments.Results: The normative data showed that speaking behavior and voice sound characteristics can be quantified in a reproducible and language-independent way. The high resolution of the method was verified by a computerized assignment of speech parameter patterns to languages at a success rate of 90%, while the correct assignment to texts was 70%. In the longitudinal self-assessment study we calculated individual 'baselines' for each test person along with deviations thereof. The significance of such deviations was assessed through the normative reference data.Conclusions: Our data provided gender-, age-, and language-specific thresholds that allow one to reliably distin-guish between 'natural fluctuations' and 'significant changes'. The longitudinal self-assessment study with repeated assessments at 1-day intervals over 14 days demonstrated the feasibility and efficiency of the speech analysis method in home environments, thus clearing the way to a broader range of applications in psychiatry.Background: Human speech is greatly influenced by the speakers' affective state, such as sadness, happiness, grief, guilt, fear, anger, aggression, faintheartedness, shame, sexual arousal, love, amongst others. Attentive listeners discover a lot about the affective state of their dialog partners with no great effort, and without having to talk about it explicitly during a conversation or on the phone. On the other hand, speech dysfunctions, such as slow, delayed or monotonous speech, are prominent features of affective disorders.
机译:方法:该项目由来自布里斯托尔(英语:n = 117),洛桑(法语:n = 128),苏黎世(德语:​​n = 208)和巴伦西亚(西班牙语:n = 124)的健康志愿者进行的四项研究组成。所有样本均根据性别,年龄和学历进行了分层。针对不同类型的口语文本的特定研究设计以及每隔14天的重复评估,使我们能够估计语音参数随时间的“自然”变化,并分析语音参数对口头形式和内容的敏感性文本。此外,我们的项目还包括一项纵向自我评估研究,该研究与来自苏黎世的大学生(n = 18)和来自瓦伦西亚的失业成年人(n = 18)一起进行,目的是测试语音分析方法在家庭环境中的可行性。数据表明,说话行为和语音特征可以以可重现且与语言无关的方式进行量化。通过将语音参数模式计算机化地分配给语言,以90%的成功率验证了该方法的高分辨率,而对文本的正确分配为70%。在纵向自我评估研究中,我们为每个测试人员计算了各自的“基准”及其偏差。结论:我们的数据提供了针对性别,年龄和语言的阈值,这些阈值使人们能够可靠地区分“自然波动”和“重大变化”。纵向自我评估研究以14天为期1天的间隔反复评估,证明了语音分析方法在家庭环境中的可行性和有效性,从而为在精神病学领域的广泛应用扫清了道路。受说话人情感状态的影响,例如悲伤,幸福,悲伤,内,恐惧,愤怒,攻击性,胆怯,羞耻,性唤起,爱情等。细心的倾听者可以毫不费力地发现很多关于其对话伙伴的情感状态的信息,而不必在对话或电话中明确地谈论它。另一方面,言语功能障碍,例如缓慢,延迟或单调的言语,是情感障碍的主要特征。

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