It is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health of an individual's cognitive ability. In fact, most neuropsychological testing batteries have a component related to speech and language where clinicians elicit speech from patients for subjective evaluation across a broad set of dimensions. With advances in speech signal processing and natural language processing, there has been recent interest in developing tools to detect more subtle changes in cognitive-linguistic function. This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of speech and language, early diagnosis of neurological disease, and tracking of disease after diagnosis. With an emphasis on cognitive and thought disorders, in this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their advantages and disadvantages. Broadly speaking, the review is split into two categories: language features based on natural language processing and speech features based on speech signal processing. Within each category, we consider features that aim to measure complementary dimensions of cognitive-linguistics, including language diversity, syntactic complexity, semantic coherence, and timing. We conclude the review with a proposal of new research directions to further advance the field.

中文翻译：

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用于评估认知和思维障碍的自动语音和语言特征综述

人们普遍认为，从分析语音（声学信号）和语言产生（单词和句子）中获得的信息是了解个人认知能力健康状况的有用窗口。事实上，大多数神经心理学测试电池都有与言语和语言相关的组件，临床医生可以从患者那里获取言语，以便在广泛的维度上进行主观评估。随着语音信号处理和自然语言处理的进步，最近人们对开发工具来检测认知语言功能的更微妙变化产生了兴趣。这项工作依赖于从录制和转录的语音中提取一组特征，以对语音和语言进行客观评估、神经系统疾病的早期诊断以及诊断后的疾病跟踪。在本文中，我们重点关注认知和思维障碍，回顾了该领域现有的语音和语言特征，讨论了它们的临床应用，并强调了它们的优点和缺点。从广义上讲，该评论分为两类：基于自然语言处理的语言特征和基于语音信号处理的语音特征。在每个类别中，我们考虑旨在衡量认知语言学互补维度的特征，包括语言多样性、句法复杂性、语义连贯性和时间安排。我们在审查结束时提出了进一步推进该领域的新研究方向的建议。