A review of depression and suicide risk assessment using speech analysis
A review of depression and suicide risk assessment using speech analysis
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DOI：
10.1016/j.specom.2015.03.004
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发表时间：
2015-07-01
影响因子：
3.2
通讯作者：
Quatieri, Thomas F.
中科院分区：
文献类型：
--
作者：
Cummins, Nicholas;Scherer, Stefan;Quatieri, Thomas F.
本文首次对语音自动分析作为抑郁和自杀的客观预测指标进行了综述。这两种情况都是主要的公共卫生问题；长期以来，抑郁症一直被认为是世界范围内造成残疾和负担的一个主要原因，而自杀是一种被误解的复杂死亡过程，它严重影响了身后家庭和社区的生活质量和心理健康。尽管如此，抑郁症的诊断和自杀风险的评估，由于其复杂的临床特征，是一项艰巨的任务，名义上是通过对一组特定症状的分类评估来实现的。然而，这两种情况的许多关键症状，比如情绪和动机的改变，本质上都不是身体上的；因此，对他们进行分类评分会给诊断过程带来一系列主观偏见。由于这些困难，寻找一套生物、生理和行为标记来帮助临床评估的研究越来越受欢迎。这篇综述首先建立了语言被认为是这两种情况的关键客观标志的案例；回顾当前抑郁症和自杀的诊断和评估方法，包括关键的非言语生物、生理和行为标记，并强调与这两种影响言语产生的疾病相关的预期认知和生理变化。然后我们回顾关键特征；主动抑郁和自杀言语数据库的大小、相关临床评分和收集范式。本文的主要重点是研究常见的副语言特征是如何受到抑郁和自杀倾向的影响，以及这些信息在分类和预测系统中的应用。本文最后对关键挑战进行了深入的讨论-通过更大的研究合作和增加数据收集的标准化来提高通用性，以及减少不必要的可变性来源，这将塑造这个快速增长的语音处理研究领域的未来研究方向。(C) 2015 Elsevier B.V.版权所有
This paper is the first review into the automatic analysis of speech for use as an objective predictor of depression and suicidality. Both conditions are major public health concerns; depression has long been recognised as a prominent cause of disability and burden worldwide, whilst suicide is a misunderstood and complex course of death that strongly impacts the quality of life and mental health of the families and communities left behind. Despite this prevalence the diagnosis of depression and assessment of suicide risk, due to their complex clinical characterisations, are difficult tasks, nominally achieved by the categorical assessment of a set of specific symptoms. However many of the key symptoms of either condition, such as altered mood and motivation, are not physical in nature; therefore assigning a categorical score to them introduces a range of subjective biases to the diagnostic procedure. Due to these difficulties, research into finding a set of biological, physiological and behavioural markers to aid clinical assessment is gaining in popularity. This review starts by building the case for speech to be considered a key objective marker for both conditions; reviewing current diagnostic and assessment methods for depression and suicidality including key non-speech biological, physiological and behavioural markers and highlighting the expected cognitive and physiological changes associated with both conditions which affect speech production. We then review the key characteristics; size, associated clinical scores and collection paradigm, of active depressed and suicidal speech databases. The main focus of this paper is on how common paralinguistic speech characteristics are affected by depression and suicidality and the application of this information in classification and prediction systems. The paper concludes with an in-depth discussion on the key challenges - improving the generalisability through greater research collaboration and increased standardisation of data collection, and the mitigating unwanted sources of variability that will shape the future research directions of this rapidly growing field of speech processing research. (C) 2015 Elsevier B.V. All rights reserved.