Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands.
University Center for Psychiatry, University Medical Center Groningen, Groningen, The Netherlands.
CNS Neurol Disord Drug Targets. 2023;22(2):152-160. doi: 10.2174/1871527320666211213125847.
Depression is a debilitating disorder that at present lacks a reliable biomarker to aid in diagnosis and early detection. Recent advances in computational analytic approaches have opened up new avenues in developing such a biomarker by taking advantage of the wealth of information that can be extracted from a person's speech.
The current review provides an overview of the latest findings in the rapidly evolving field of computational language analysis for the detection of depression. We cover a wide range of both acoustic and content-related linguistic features, data types (i.e., spoken and written language), and data sources (i.e., lab settings, social media, and smartphone-based). We put special focus on the current methodological advances with regard to feature extraction and computational modeling techniques. Furthermore, we pay attention to potential hurdles in the implementation of automatic speech analysis.
Depressive speech is characterized by several anomalies, such as lower speech rate, less pitch variability and more self-referential speech. With current computational modeling techniques, such features can be used to detect depression with an accuracy of up to 91%. The performance of the models is optimized when machine learning techniques are implemented that suit the type and amount of data. Recent studies now work towards further optimization and generalizability of the computational language models to detect depression. Finally, privacy and ethical issues are of paramount importance to be addressed when automatic speech analysis techniques are further implemented in, for example, smartphones. Altogether, computational speech analysis is well underway towards becoming an effective diagnostic aid for depression.
抑郁症是一种使人虚弱的疾病，目前缺乏可靠的生物标志物来帮助诊断和早期发现。计算分析方法的最新进展为开发此类生物标志物开辟了新途径，利用从一个人的言语中提取的丰富信息。
本综述概述了计算语言分析在检测抑郁症方面的快速发展领域中的最新发现。我们涵盖了广泛的声学和与语言内容相关的语言特征、数据类型（即口语和书面语）以及数据源（即实验室环境、社交媒体和基于智能手机的环境）。我们特别关注当前在特征提取和计算建模技术方面的最新方法进展。此外，我们还关注自动语音分析实施中的潜在障碍。
抑郁言语的特点是存在多种异常，例如语速较低、音高变化较少、自我参照性言语较多。使用当前的计算建模技术，这些特征可以以高达 91%的准确率来检测抑郁症。当实施适合数据类型和数量的机器学习技术时，模型的性能会得到优化。最近的研究现在致力于进一步优化和推广计算语言模型，以检测抑郁症。最后，当自动语音分析技术进一步应用于智能手机等设备时，隐私和道德问题至关重要。总之，计算语音分析在成为抑郁症的有效诊断辅助手段方面进展顺利。
